CN103106323A - Method and device for obtaining graph structural description information - Google Patents

Abstract

An embodiment of the invention discloses a method for obtaining graph structural description information. The method includes that a structure of a graph is extracted in a given graph; the structure of the graph is converted into an information flow graph, the information flow graph is used for representation of an information flow space defined by the structure of the graph, and each node in the structure of the graph carries out information flow according to information flow rules in the information flow graph; the initial information amount of each node in the information flow graph is determined in the structure of the graph; according to the initial information amount of each node, according to the information flow rules, the information amount of each node at a preset time point is calculated; and according to a time sequence of information distribution formed by information amounts of all the preset time points, the structural description information of the graph is determined. The method for obtaining the graph structural description information achieves dynamic analysis for the structure of the graph, dynamic description information of the structure of the graph is obtained, the information amount of the structural description information of the graph is increased, and the structure of the graph can be effectively described.

Description

Obtain method and the device of the structure descriptor of figure

Technical field

Relate generally to technical field of image processing of the present invention, especially a kind of method and device that obtains the structure descriptor of figure.

Background technology

In image processing process, often need to extract the structure of figure in image, for example, extract circuit structure in the electronic circuit from electronic applications, as shown in Figure 1, again for example, from various network architectures (as communication network, internet, the network of communication lines), extract network data structure, extract molecular structure or DNA, RNA structure etc. in chemical field or biological field.

After extracting the structure of figure, further can obtain the descriptor of the structure of figure, to be used for comparing fast, sorting and searching etc. figure.Wherein, the descriptor of the structure of figure by the limit of a group node and one group of connected node and be attached to node and the limit on information structure.

In prior art, extract the structure of figure from image after, by the numeric representation method of employing based on the structure of the figure of graph theory, the method (graph entropy) of graph-based spectral analysis method (graph spectrum method) and graph-based entropy etc. is come the structure descriptor of presentation graphs.Yet, all being based in above-mentioned method for expressing the static analysis of the structure of figure and the quiescent value that obtains, its analytic process is too simple, and the quiescent value that obtains is the structural information of phenogram fully, can not effectively describe the structure of figure.

Summary of the invention

In view of this, the embodiment of the present invention provides method and the device of the structure descriptor of a kind of figure of acquisition, the better structural information of phenogram, the structure of more effective description figure.

According to an aspect of the embodiment of the present invention, the method for the structure descriptor of a kind of figure of acquisition is provided, comprising:

Extract the structure of figure in the Given Graph picture;

The structure of described figure is converted to the information flow cardon, and described information flow cardon is in order to characterize the information flow space by the organization definition of described figure, and in the structure of described figure, each node carries out information flow according to the information flow rule in described information flow cardon;

Determine the initial information amount of each node in described information flow cardon in the structure of described figure;

According to the initial information amount of described each node, according to described information flow rule, calculate described each node in the quantity of information of Preset Time point;

According to by the time series of described each node in the formed information distribution of the quantity of information of all Preset Time points, determine the structure descriptor of described figure.

According to another aspect of the embodiment of the present invention, a kind of graph structure control methods is provided, comprising:

By the method according to the invention described above embodiment, obtain the structure descriptor of at least two figure to be contrasted;

If the structure descriptor of described at least two figure is identical, the structure of at least two figure described to be contrasted is identical.

According to another aspect of the embodiment of the present invention, the device of the structure descriptor of a kind of figure of acquisition is provided, comprising:

The structure extraction unit is configured to the structure of extraction figure in the Given Graph picture;

The figure converting unit, be configured to the structure of described figure is converted to the information flow cardon, described information flow cardon is in order to characterize the information flow space by the organization definition of described figure, and in the structure of described figure, each node carries out information flow according to the information flow rule in described information flow cardon;

The initial value determining unit is configured to determine the initial information amount of each node in described information flow cardon in the structure of described figure;

The information computing unit is configured to the initial information amount according to described each node, according to described information flow rule, calculates described each node in the quantity of information of Preset Time point;

The information determining unit is configured to determine the structure descriptor of described figure according to by the time series of described each node in the formed information distribution of the quantity of information of all Preset Time points.

In addition, according to a further aspect in the invention, also provide a kind of storage medium.Described storage medium comprises machine-readable program code, and when carrying out described program code on messaging device, described program code makes described messaging device execution according to the method for the structure descriptor of above-mentioned acquisition figure of the present invention.

In addition, in accordance with a further aspect of the present invention, also provide a kind of program product.Described program product comprises the executable instruction of machine, and when carrying out described instruction on messaging device, described instruction makes described messaging device execution according to the method for the structure descriptor of above-mentioned acquisition figure of the present invention.

Said method according to the embodiment of the present invention, after the structure of extracting figure, be the information flow cardon by the thaumatropy with figure, the quantity of information that obtains each node and time correlation distributes, realize the performance analysis to the structure of figure, and then obtained the dynamic descriptor of the structure of figure, thereby increased the quantity of information of the structure descriptor of figure, fully characterize the structure of figure, can more effectively describe the structure of figure.

Provide other aspects of the embodiment of the present invention in instructions part below, wherein, describe the preferred embodiment that is used for disclosing fully the embodiment of the present invention in detail, and it is not applied restriction.

Description of drawings

Below in conjunction with specific embodiment, and with reference to accompanying drawing, the above-mentioned of the embodiment of the present invention and other purpose and advantage are further described.In the accompanying drawings, identical or technical characterictic or parts correspondence will adopt identical or corresponding Reference numeral to represent.

Fig. 1 illustrates the schematic diagram that extracts circuit structure in prior art from electronic circuit;

Fig. 2 is the method flow diagram that the structure descriptor of the acquisition figure that provides as the embodiment of the present invention is shown;

The method flow diagram that the structure with figure that provides as the embodiment of the present invention is converted to Markov figure is provided Fig. 3;

Fig. 4 a illustrates the structural representation that a kind of figure is provided as the embodiment of the present invention;

Fig. 4 b illustrates the schematic diagram that the Markov figure after the thaumatropy of Fig. 4 a is provided as the embodiment of the present invention;

Fig. 5 illustrates the structural representation that a kind of figure is provided as the embodiment of the present invention;

Fig. 6 illustrates the structural representation that a kind of figure is provided as the embodiment of the present invention;

Fig. 7 illustrates as the embodiment of the present invention to provide the method flow diagram of determining each node initial information amount;

Fig. 8 illustrates each node of calculating of providing as the embodiment of the present invention at the method flow diagram of the quantity of information of Preset Time point;

Fig. 9 is the method flow diagram that the structure descriptor of the definite figure that provides as the embodiment of the present invention is shown;

Figure 10 illustrates another that provide as the embodiment of the present invention to determine the method flow diagram of the structure descriptor of figure;

The method flow diagram that the graph-based structure descriptor that provides as the embodiment of the present invention is carried out the comparison of graph structure is provided Figure 11;

Figure 12 is the apparatus structure schematic diagram that the structure descriptor of a kind of figure of acquisition that provides as the embodiment of the present invention is shown;

Figure 13 is the structural representation that a kind of information computing unit that provides as the embodiment of the present invention is shown;

Figure 14 is the structural representation that a kind of information determining unit that provides as the embodiment of the present invention is shown;

Figure 15 is the structural representation that a kind of initial value determining unit that provides as the embodiment of the present invention is shown;

Figure 16 is the structural representation that the another kind of information determining unit that provides as the embodiment of the present invention is shown;

Figure 17 is the block diagram that illustrates as the exemplary configurations of the personal computer of the messaging device that adopts in embodiments of the invention.

Embodiment

Embodiments of the invention are described with reference to the accompanying drawings.

Referring to Fig. 2, the method for the structure descriptor of the acquisition figure that the embodiment of the present invention provides comprises:

Step 201: the structure of extracting figure in the Given Graph picture.

in the present embodiment, at first the structure that needs acquisition figure from image, for example shown in Figure 1, (for example extract the structure of circuit in the electronic circuit image, Goto, S., " An efficient algorithmfor the two-dimensional placement problem in electrical circuitlayout ", IEEE Transactions on Circuits and Systems, vol.28, pp.12-18, 1981), perhaps, from various network architectures (as communication network, the internet, the network of communication lines) (for example extract network data structure, Schenker, A., " Graph-Theoretic Techniques for Web CotentMining ", World Scientific, Singapore, 2005), extract molecular structure or DNA in chemical field or biological field, the RNA structure (for example, Faulon, J.L., " Isomorphism, automorphism partitioning, and canonical labeling can be solved inpolynomial-time for mocular graphs ", Journal of Chemical Informationand Computer Sciences, vol.38, pp.432-444, 1998), natural language processing field (is for example extracted syntactic structure, Biemann, C., " Chinese whispers:an efficient graphclustering algorithm and its application to natural language processingproblems ", Processing of the First Workshop on Graph Based Methodsfor Natural Language Processing, pp.73-80, 2006), and other is such as word, fingerprint, image (for example, Riesen, K., " IAM graph database repository for graphbased pattern recognition and machine learning ", Structure, Syntactic, and Statistical Pattern Recognition, pp.287-297, 2008) etc.Each leaching process can adopt prior art, and its detailed process repeats no more.

Step 202 is converted to the information flow cardon with the structure of figure.

After obtaining the structure of figure, according to the node in the structure of figure and internodal line, the structure of this figure is converted to the information flow cardon, so that follow-uply based on this information flow cardon, the structure of this figure is carried out performance analysis, obtain dynamic descriptor.

Wherein, this information flow cardon is in order to characterize the information flow space by the organization definition of figure, and in the structure of figure, each node carries out information flow according to the information flow rule in the information flow cardon.

Step 203 is determined the initial information amount of each node in the information flow cardon in the structure of figure.

Before the quantity of information of each node flows according to the information flow rule or propagates, need first to determine the initial information amount of each node, this initial information amount determine can with the structure-irrelevant of figure, such as directly determining according to predefined rule (as univesral distribution) etc., also can the graph-based structure determine, for example, if the structure of figure is the non-regular graph structure, the initial information amount of each node is distributed according to the principle of univesral distribution, the initial information amount that is also each node is identical, equals gross information content at the mean value of each node; If the structure of figure is the regular graph structure, when determining the initial information amount of each node, can select one of them node as start node, determine that then the initial information amount of this start node is 1, the initial information amount of other node is 0, and the selection of this start node can be arbitrarily.Further, for the complete descriptor of the structure of obtaining this figure, the selection of this start node need to repeating query between each node.Above dual mode specifically please refer to the description of subsequent embodiment.

Step 204 according to the initial information amount of each node, according to the information flow rule, is calculated each node in the quantity of information of Preset Time point.

After the initial information amount of determining each node, in the information flow cardon of the structural correspondence of figure, the quantity of information of each node flows according to information flow rule start information or propagates, and not in the same time or the Preset Time point, can obtain each node in the quantity of information of current time.Wherein, default time point has a plurality of, can set as required, for example, if setting the initial time of node is first Preset Time point, the initial information amount that is also node is the nodal information amount under the first Preset Time point, and the quantity of default time point can be identical with the quantity of node, can certainly be different.As for can set as required the interval time between time point, concrete when obtaining the quantity of information of each Preset Time point lower node, can and time point between the interval have nothing to do.

Step 205 according to by the time series of each node in the formed information distribution of the quantity of information of all Preset Time points, is determined the structure descriptor of figure.

Obtain respectively each node after the quantity of information of different Preset Time points, the time series that each node all can a corresponding quantity of information distributes according to the time series that the quantity of information of all nodes distributes, can be determined the structure descriptor of figure.Particularly, can be directly with the time series that the obtains descriptor as the structure of this figure, also can carry out certain processing to this time series, as sequence etc., then with the time series after sequence, a certain sequence of perhaps selecting after the sequence is as the descriptor of the structure of figure.

The embodiment of the present invention is after the structure of extracting figure, be the information flow cardon by the thaumatropy with figure, the quantity of information that obtains each node and time correlation distributes, realized the performance analysis to the structure of figure, and then obtained the dynamic descriptor of the structure of figure, thereby increased the quantity of information of the structure descriptor of figure, fully characterized the structure of figure, can more effectively describe the structure of figure.

Extract the structure of figure in the Given Graph picture after, be the information flow cardon with the thaumatropy of figure, in one exemplary embodiment of the present invention, this information flow cardon describes as an example of Markov figure example, the structure of this figure is converted to the process of Markov figure, as shown in Figure 3, can comprise the following steps:

Step 301 increases the directed edge that points to oneself on each node in the structure of figure.

The structure of figure describes as an example of Fig. 4 a, 4b example, comprises a, b, c, four nodes of d in the structure of this figure, increases the directed edge that points to oneself on each node, for example the limit of node a sensing oneself in Fig. 4 b.

Step 302, in the structure according to figure, internodal line is determined corresponding internodal directed edge.

As shown in Fig. 4 a, there is line between node a, b and between a, d, and the two equal aphalangias of line to, also be nonoriented edge, two lines are distinguished corresponding two directed edges, a as corresponding in line between ab points to the directed edge of b and the directed edge that b points to a, and the corresponding a of the line between ad points to the directed edge of d and the directed edge that d points to a, and other internodal line is similar.

In another embodiment, if two internodal lines are directed edge, between this line corresponding directed edge only, this directed edge and node, the sensing of line is identical.

Wherein, the order of step 301 and step 302 can be adjusted.

Step 303 calculates based on the original tally distribution of line between node and node the weight that obtains each directed edge.

In obtaining the structure of figure after all directed edge, exist the original tally of line between node and node to distribute in the structure of figure, for example in Fig. 4 a, the original tally of node a is 1, and the original tally of node d is 4, ..., the original tally of line ab is 5, and the original tally of line ad is 7 ..., distribute according to this original tally, can calculate the weight that obtains each directed edge.

When Determining Weights, at first to the weight normalization on each node, for example, mark total value on node a is 1 (mark of node a)+5 (mark of limit ab)+6 (mark of limit ac)+7 (mark of limit ad)=19, similarly, mark total value on node b is 5+2+8=15, and the mark total value of node c is 18, and the mark total value of node d is 28.

Then, calculate the weights W of each directed edge.wherein, the weights W of directed edge is that the mark total value on the node that flows out according to the mark value of the corresponding line of this directed edge and this directed edge is calculated, for example, a is to flow out node a's to the directed edge of b, a is (mark value of ab line)/(the mark total value of node a)=5/19 to the weights W ab of the directed edge of b, and b is to flow out node b's to the directed edge of a, b is (mark value of ab line)/(the mark total value of node b)=5/15 to the weights W ba of the directed edge of a, similarly, Wac=6/19, Wca=6/18, Wcd=9/18, Wdc=9/28, Wbd=8/15, Wdb=8/28.In addition, for the directed edge that points to self, similarly calculating can obtain, and Waa, Wbb, Wcc, Wdd are followed successively by 1/19,2/15,3/18,4/28.

Through after above three steps, can obtain the Markov figure after the thaumatropy of Fig. 4 a, as shown in Fig. 4 b.

The structure of figure for other adopts above-mentioned steps can obtain too its Markov figure, as shown in Figure 5.In addition, for the structure that there is no the figure of the mark value of line between clear and definite flag node and node, as shown in Figure 6, the original tally that can be defaulted as its node is 1, and between node, the original tally of connecting line is 1, and its corresponding Markov figure as shown in Figure 6.

In the thus obtained structure that comprises figure, each node points to the directed edge of oneself, the corresponding directed edge of internodal line, and based on the distribute Markov figure of the weight of calculating each directed edge that obtains of the original tally of line between node and node, can be used as the information flow cardon of the structure of figure.In this Markov figure, the rule of information flow is the quantity of information of each node and carries out information flow according to the weight of internodal directed edge and directed edge.

After the thaumatropy with figure is the information flow cardon, in the structure of figure, the quantity of information of each node flows according to the flow rule in the information flow cardon, before carrying out information flow, need first to determine the initial information amount of each node, in one exemplary embodiment of the present invention, as shown in Figure 7, the method for determining each node initial information amount can comprise the following steps:

Step 701, the structure of judgement figure are non-regular graph structure or regular graph structure.

In the present embodiment, come the initial information amount of decision node according to the structure of figure.The quantity on the limit that each node flows out in the structure of figure is identical, and each node weight of flowing out the limit is when also identical, and this figure is regular graph, otherwise is non-regular graph.

Step 702, if the non-regular graph structure, with the gross information content of information flow cardon in the mean value of each node initial information amount as each node.

If the non-regular graph structure, can think that the initial information amount of each node is identical in the structure of figure, be gross information content at the mean value of all nodes, for example Fig. 4 a, the non-regular graph that is shown in Figure 6.

Step 703 is if the regular graph structure is selected start node in each node.

In regular graph, the selection of this start node is arbitrarily, and for the complete descriptor of the structure of obtaining this figure, the selection of this start node need to repeating query between each node.

Step 704, the initial information amount with start node in the information flow cardon is defined as 1, and the initial information amount of other node in the information flow cardon is defined as 0.

Be as shown in Figure 5 regular graph.

After the initial information amount of determining node, further according to the initial information amount of each node, according to the information flow rule, calculate each node in the quantity of information of Preset Time point, in one exemplary embodiment of the present invention, calculate each node in the process of the quantity of information of Preset Time point, as shown in Figure 8, can comprise the following steps:

Step 801, the quantity of information of calculating respectively each node current preset time point flow to the product of weight of the directed edge of a certain node with it.

At first, suppose that node j is Pjt in the quantity of information of current preset time point t, the weight that this node j flows to the directed edge of node i is Wji, the node j product of weight that flows to the directed edge of node i in t quantity of information constantly with it is PjtWji, wherein, node i, j is the node in the structure of figure, i, j only are used for identification nodes, node i, j can be same node (directed edge that this moment, node j flowed to node i also i.e. this node point to oneself directed edge), can be also different nodes.

For example, the initial information amount of node j is Pj0, is also that the quantity of information of node j under first Preset Time point (t0) is Pj0.The weight that this node j flows to the directed edge of node i is Wji.

The node j product of weight that flows to the directed edge of node i in t0 quantity of information constantly with it is PjoWji.

Step 802, the sum product that each node of obtaining is corresponding is as the quantity of information of this a certain node at next Preset Time point.

Suppose total n node in the structure of this figure, node i is at the quantity of information Pit+1 of next Preset Time point t+1

For example, the quantity of information Pi1 of node i under second Preset Time point (t1) is

According to the method described above, as long as determine the initial information amount of each node, can obtain each node in n the Preset Time point (quantity of information under t0～tn-1).Wherein, the initial information amount of each node can be predefined, can be based on also that the structure of figure determines, such as regular graph or non-regular graph etc.

After obtaining the quantity of information of each node at Preset Time point, can obtain the time series that each nodal information distributes, and then can determine accordingly the structure descriptor of figure.In one exemplary embodiment of the present invention, if the structure of this figure is the non-regular graph structure, should determine the process of the structure descriptor of figure, as shown in Figure 9, can comprise the following steps:

Step 901 obtains respectively the time series that each node distributes in the formed information of the quantity of information of all Preset Time points.

In the present embodiment, for the information of the structure that obtains to be enough to description figure, the quantity of regulation Preset Time point equals the quantity of node in the structure of figure at least, is also in Preset Time point t0～tn-1, and n just equals the quantity of node.

According to preceding method, node i n Preset Time point (quantity of information under t0～tn-1) is: Pi0, Pi1 ..., Pin-1.For non-regular graph, because the initial information amount of each node is identical, be univesral distribution, so, the time series that in the structure of figure, all nodes distribute in the formed information of the quantity of information of all Preset Time points, an available matrix representation is:

$\left(\begin{array}{cccc}{P}_{10}& P_{11}& ...& P_{1n-1}\\ P_{20}& P_{21}& ...& P_{2n-1}\\ ...& ...& ...& ...\\ P_{n0}& {\mathrm{<figure-callout\; id="1"\; label="P\; n"\; filenames="HSA00000620939400022.png"\; state="\{\{state\}\}">P</figure-callout>}}_n& ...& P_{\mathrm{nn}-1}\end{array}\right)$

Describe as an example of Fig. 4 a, 4b example, the structure of this figure is non-regular graph, the initial information amount univesral distribution between node a, b, c, d, Pa0=Pb0=Pc0=Pd0=1/4 (quantity of information under the first Preset Time point t0).

Based on aforementioned formula: node i is in t+1 quantity of information constantly

As can be known:

According to the weight of each directed edge as shown in Fig. 4 b, the quantity of information of each node is distributed as under second Preset Time point (t1):

Pa1＝Waa＊Pa0+Wba＊Pb0+Wca＊Pc0+Wda＊Pd0＝0.242325

Pb1＝Wbb＊Pb0+Wab＊Pa0+Wdb＊Pd0＝0.170551

Pc1＝Wcc＊Pc0+Wac＊Pa0+Wdc＊Pd0＝0.200971

Pd1＝Wdd＊Pd0+Wad＊Pa0+Wbd＊Pb0+Wcd＊Pc0＝0.386153

The quantity of information of lower each node of the 3rd Preset Time point (t2) is distributed as:

Pa2＝Waa＊Pa1+Wba＊Pb1+Wca＊Pc1+Wda＊Pd1＝0.233133

Pb2＝Wbb＊Pb1+Wab＊Pa1+Wdb＊Pd1＝0.196839

Pc2＝Wcc＊Pc1+Wac＊Pa1+Wdc＊Pd1＝0.234139

Pd2＝Wdd＊Pd1+Wad＊Pa1+Wbd＊Pb1+Wcd＊Pc1＝0.335888

The quantity of information that by that analogy, can obtain each node under the 4th Preset Time point (t3) is distributed as:

Pa3, Pb3, Pc3, Pd3 are followed successively by 0.239902,0.183564,0.220608,0.355926

In this Fig. 4 a, all nodes in the time series that the formed information of the quantity of information of all Preset Time points distributes are:

$\left(\begin{array}{cccc}{P}_{a0}& P_{a1}& P_{a2}& P_{a3}\\ P_{b0}& P_{b1}& P_{b2}& P_{b3}\\ P_{c0}& P_{c1}& P_{c2}& P_{c3}\\ P_{d0}& P_{d1}& P_{d2}& {\mathrm{<figure-callout\; id="3"\; label="P\; d"\; filenames="HSA00000620939400022.png"\; state="\{\{state\}\}">P</figure-callout>}}_d\end{array}\right)=\left(\begin{array}{cccc}0.25& 0.242325& 0.233133& 0.239902\\ 0.25& 0.170551& 0.196839& 0.183564\\ 0.25& 0.200971& 0.234139& 0.220608\\ 0.25& 0.386153& 0.335888& 0.355926\end{array}\right)$

Step 902, the time series that the information of each node is distributed sorts according to the first pre-defined rule.

In order to obtain the information distribution time sequence of structure that can unique this figure of expression, can also carry out this step, the time series that the information of each node is distributed sorts, wherein, the the first performed pre-defined rule that sorts can be the predefined ordering rule that obtains unique ranking results, such as sorting etc. to the value of correspondence position in sequence is descending or ascending successively according to the predeterminated position order.In the present embodiment, quantity of information sequence ascending sort of sequencing at preset timed intervals to each node wherein, due to Pa0=Pb0=Pc0=Pd0, sorted according to the value of Pa1, Pb1, Pc1, Pd1 is ascending, can get:

$\left(\begin{array}{cccc}0.25& 0.170551& 0.196839& 0.183564\\ 0.25& 0.200971& 0.234139& 0.220608\\ 0.25& 0.242325& 0.233133& 0.239902\\ 0.25& 0.386153& 0.335888& 0.355926\end{array}\right)$

Step 903 is with the structure descriptor of the time series after sequence as this figure.

Time series after this sequence namely can be used as the descriptor of the structure of this figure, and this seasonal effect in time series concrete manifestation form, can be the form with above-mentioned matrix certainly, can be also sequence of being arranged in order of numerical value etc., to this form of expression, is not construed as limiting herein.Due to the time series after this sequence can the only table diagram structure, use the time series after this sequence, the structure of figure that can be more different.

Certainly, in other embodiments, if do not require the structure descriptor that obtains unique this figure of expression, also namely need not to obtain the information distribution time sequence of the structure of unique this figure of expression, can omit step 902.

In another exemplary embodiment of the present invention, if the structure of this figure is the regular graph structure, should determine the process of the structure descriptor of figure, as shown in figure 10, can comprise the following steps:

Step 1001, respectively with different nodes as start node, obtain start node and other node in the time series of the formed information distribution of the quantity of information of all Preset Time points.

Differently from non-regular graph in the present embodiment be, in non-regular graph, the initial information amount of node is according to univesral distribution, and each initial information amount is identical, and obtainable each node is distributed as a matrix in the quantity of information of Preset Time point.And for regular graph, because the initial information amount of each node is not univesral distribution, each node can be as start node, so, the matrix that corresponding different quantity of information distributes during as start node for different nodes, that is to say, if n node arranged, corresponding n different quantity of information distribution matrix.

In the present embodiment with non-regular graph be similarly, in the time series that obtains information distribution corresponding to each start node, information for the structure that obtains to be enough to description figure, the quantity of the Preset Time point that adopts equals the quantity of node in the structure of figure at least, be also in Preset Time point t0～tn-1, n just equals the quantity of node.Node i n Preset Time point (quantity of information under t0～tn-1) is: Pi0, Pi1 ..., Pin-1.In the structure of this figure when arbitrary node during as start node, the time series that all nodes distribute in the formed information of the quantity of information of all Preset Time points, equal available matrix representations is:

$\left(\begin{array}{cccc}{P}_{10}& P_{11}& ...& P_{1n-1}\\ P_{20}& P_{21}& ...& P_{2n-1}\\ ...& ...& ...& ...\\ P_{n0}& {\mathrm{<figure-callout\; id="1"\; label="P\; n"\; filenames="HSA00000620939400022.png"\; state="\{\{state\}\}">P</figure-callout>}}_n& ...& P_{\mathrm{nn}-1}\end{array}\right)$

Different nodes are during as start node, and corresponding matrix is different.

In this step, at first with different nodes as start node, obtain the time series that all information distributes.Below, describe as an example of regular graph shown in Figure 5 example.

In Fig. 5, the original tally of each node is 1, and the weight that each node flows out the quantity on limit and respectively flows out the limit is identical (being 1/4) all.

When selecting node a as start node, Pa0=1, Pb0=Pc0=Pd0=Pe0=Pf0=0 (quantity of information under the first Preset Time point t0).

Based on aforementioned formula: node i is in t+1 quantity of information constantly

As can be known:

Be 1/4 as can be known according to the weight of each directed edge in Fig. 5, the quantity of information of each node is distributed as under second Preset Time point (t1):

Pa1＝Waa＊Pa0+Wba＊Pb0+Wca＊Pc0+Wfa＊Pf0＝0.25

Pb1＝Wbb＊Pb0+Wab＊Pa0+Wcb＊Pc0+Web＊Pe0＝0.25

Pc1＝Waa＊Pa0+Wba＊Pb0+Wca＊Pc0+Wfa＊Pf0＝0.25

Pd1＝Wdd＊Pd0+Wcd＊Pc0+Wed＊Pe0+Wfd＊Pf0＝0

Pe1＝Wee＊Pe0+Wbe＊Pb0+Wde＊Pd0+Wfe＊Pf0＝0

Pf1＝Wff＊Pf0+Waf＊Pa0+Wef＊Pe0+Wdf＊Pd0＝0.25

By that analogy, can obtain the 3rd to the 6th Preset Time point (quantity of information of lower each node of t2～t5) is distributed as:

Pa2, Pb2, Pc2, Pd2, Pe2, Pf2 are followed successively by 0.25,0.1875,0.1875,0.125,0.125,0.125

Pa3, Pb3, Pc3, Pd3, Pe3, Pf3 are followed successively by 0.1875,0.1875,0.1875,0.1406,0.1406,0.1562

Pa4, Pb4, Pc4, Pd4, Pe4, Pf4 are followed successively by 0.1797,0.1758,0.1758,0.1562,0.1562,0.1562

Pa5, Pb5, Pc5, Pd5, Pe5, Pf5 are followed successively by 0.1719,0.1719,0.1719,0.1611,0.1611,0.1621

When selecting node a as start node, all nodes in the time series that the formed information of the quantity of information of all Preset Time points distributes are:

$\left(\begin{array}{cccccc}{P}_{a0}& P_{a1}& P_{a2}& P_{a3}& P_{a4}& P_{a5}\\ P_{b0}& P_{b1}& P_{b2}& P_{b3}& P_{b4}& P_{b5}\\ P_{c0}& P_{c1}& P_{c2}& P_{c3}& P_{c4}& P_{c5}\\ P_{d0}& P_{d1}& P_{d2}& P_{d3}& P_{d4}& P_{d5}\\ P_{e0}& P_{e1}& P_{e2}& P_{e3}& P_{e4}& P_{e5}\\ P_{f0}& P_{f1}& P_{f2}& P_{f3}& P_{f4}& {\mathrm{<figure-callout\; id="5"\; label="P\; f"\; filenames="HSA00000620939400022.png"\; state="\{\{state\}\}">P</figure-callout>}}_f\end{array}\right)=\left(\begin{array}{cccccc}1& 0.25& 0.25& 0.1875& 0.1797& 0.1719\\ 0& 0.25& 0.1875& 0.1875& 0.1758& 0.1719\\ 0& 0.25& 0.1875& 0.1875& 0.1758& 0.1719\\ 0& 0& 0.125& 0.1406& 0.1532& 0.1611\\ 0& 0& 0.125& 0.1406& 0.1562& 0.1611\\ 0& 0.25& 0.125& 0.1562& 0.1562& 0.1621\end{array}\right)$

With reference to above computing method, can calculate successively acquisition: during as start node (Pb0=1, Pa0=Pc0=Pd0=Pe0=Pf0=0), all nodes are in the time series of the formed information distribution of the quantity of information of all Preset Time points with node b; With node c during as start node (Pc0=1, Pa0=Pb0=Pd0=Pe0=Pf0=0), the time series that all nodes distribute in the formed information of the quantity of information of all Preset Time points; With node d during as start node (Pd0=1, Pa0=Pb0=Pc0=Pe0=Pf0=0), the time series that all nodes distribute in the formed information of the quantity of information of all Preset Time points; With node e during as start node (Pe0=1, Pa0=Pb0=Pc0=Pd0=Pf0=0), the time series that all nodes distribute in the formed information of the quantity of information of all Preset Time points; With node f during as start node (Pf0=1, Pa0=Pb0=Pc0=Pd0=Pe0=0), the time series that all nodes distribute in the formed information of the quantity of information of all Preset Time points.

Step 1002, the time series that the information of each node corresponding to identical start node is distributed sorts according to the second pre-defined rule.

In order to obtain the information distribution time sequence of structure that can unique this figure of expression, can also carry out this step, the time series that the information of each node corresponding to identical start node is distributed sorts respectively, wherein, the the first performed pre-defined rule that sorts can be the predefined ordering rule that obtains unique ranking results, such as sorting etc. to the value of correspondence position in sequence is descending or ascending successively according to the predeterminated position order.In the present embodiment, at preset timed intervals sequencing is to ascending sequence of quantity of information sequence of each node.

The information distribution time sequence of each corresponding node describes as example take node a as start node.

To this time series $\left(\begin{array}{cccccc}1& 0.25& 0.25& 0.1875& 0.1797& 0.1719\\ 0& 0.25& 0.1875& 0.1875& 0.1758& 0.1719\\ 0& 0.25& 0.1875& 0.1875& 0.1758& 0.1719\\ 0& 0& 0.125& 0.1406& 0.1532& 0.1611\\ 0& 0& 0.125& 0.1406& 0.1562& 0.1611\\ 0& 0.25& 0.125& 0.1562& 0.1562& 0.1621\end{array}\right),$ According to the sequencing of Preset Time point ascending sequence of value to the quantity of information sequence of each node, at first, sort ascending according to the value of Pa0, Pb0, Pc0, Pd0, Pe0, Pf0, because the value of Pb0, Pc0, Pd0, Pe0, Pf0 is identical, continue to sort according to the value of Pb1, Pc1, Pd1, Pe1, Pf1 is ascending, by that analogy, the rear start node that finally can sort is that the time series of a is:

$\left(\begin{array}{cccccc}0& 0& 0.125& 0.1406& 0.1562& 0.1611\\ 0& 0& 0.125& 0.1406& 0.1562& 0.1611\\ 0& 0.25& 0.125& 0.1562& 0.1562& 0.1621\\ 0& 025& 0.1875& 0.1875& 0.1758& 0.1719\\ 0& 0.25& 0.1875& 0.1875& 0.1758& 0.1719\\ 1& 0.25& 0.25& 0.1875& 0.1797& 0.1719\end{array}\right),$ For convenience of description, this time series with $\mathrm{Pa}=\left(\begin{array}{c}{P}_{\mathrm{dt}}\\ P_{\mathrm{et}}\\ P_{\mathrm{ft}}\\ P_{\mathrm{bt}}\\ P_{\mathrm{ct}}\\ P_{\mathrm{at}}\end{array}\right)$ Represent, can certainly be described as Pa=(Pdt Pet Pft Pbt Pct Pat).

The time series take other node as start node after in like manner can obtaining to sort: Pb, Pc, Pd, Pe, Pf.

Step 1003, according to presetting selective rule, the time series that the information of each node that a certain start node after selecting to sort is corresponding distributes is as the structure descriptor of figure.

Can obtain an ordered series of numbers that depends on the figure of each node according to above step represents, further, can select the sequence of a specific figure from the relevant sequence of n node, remove the dependence to node location, this selective rule can be made an appointment as required, after for example the time series take each node as start node that is described as sequence form being sorted, fixedly selection j sequence wherein is as the structure descriptor of this figure, simply, this j sequence can be first sequence, is also minimum one.In the present embodiment, due to Pa, Pb, Pc, Pd, Pe and Pf are all identical, so use (Pdt Pet Pft Pbt Pct Pat) as the structure descriptor of this figure.

Certainly, in other embodiments, if do not require the structure descriptor that obtains unique this figure of expression, also namely need not to obtain the information distribution time sequence of the structure of unique this figure of expression, can omit step 1002.

The embodiment of the present invention is passed through said method, after the structure of extracting figure, be the information flow cardon by the thaumatropy with figure, the quantity of information that obtains each node and time correlation distributes, realize the performance analysis to the structure of figure, and then obtained the dynamic descriptor of the structure of figure, thereby increased the quantity of information of the structure descriptor of figure, fully characterize the structure of figure, can more effectively describe the structure of figure.

After obtaining the structure descriptor of figure based on above method, in another exemplary embodiment of the present invention, can also graph-based structure descriptor carry out the comparison of graph structure, method as shown in figure 11 can comprise:

Step 1101 obtains the structure descriptor of at least two figure to be contrasted.

When whether the structure of more a plurality of figure of needs was identical, at first the also i.e. homotype problem of figure obtained the structure descriptor of all figure according to preceding method, also namely obtain a time series or matrix.

Step 1102, whether the structure descriptor of comparison diagram is identical.

If identical, the structure of key diagram is identical, if different, the structure of key diagram is different.

The structure that the comparative approach of the structure of above figure can be used for two figure or a plurality of figure compares.

Be more than the description to the inventive method embodiment, the below is introduced the device embodiment that realizes said method.

Referring to Figure 12, obtain the apparatus structure schematic diagram of the structure descriptor of figure for the embodiment of the present invention is a kind of.

This device can comprise:

Structure extraction unit 1201 is configured to the structure of extraction figure in the Given Graph picture.

Figure converting unit 1202, be configured to the structure of figure is converted to the information flow cardon, this information flow cardon is in order to characterize the information flow space by the organization definition of described figure, and in the structure of figure, each node carries out information flow according to the information flow rule in described information flow cardon.

Initial value determining unit 1203 is configured to determine the initial information amount of each node in the information flow cardon in the structure of figure.

Information computing unit 1204 is configured to the initial information amount according to each node, according to the information flow rule, calculates each node in the quantity of information of Preset Time point.

Information determining unit 1205 is configured to determine the structure descriptor of figure according to by the time series of each node in the formed information distribution of the quantity of information of all Preset Time points.

Structure extraction unit 1201 extracts the structure of figure from the Given Graph picture after, by figure converting unit 1202 according to the node in the structure of figure and internodal line, the structure of this figure is converted to the information flow cardon, so that follow-uply based on this information flow cardon, the structure of this figure is carried out performance analysis, obtain dynamic descriptor.Before the quantity of information of each node flowed according to the information flow rule or propagates, initial value determining unit 1203 was first determined the initial information amount of each node, this initial information amount determine can with the structure-irrelevant of figure, also can the graph-based structure determine.For example, if the structure of figure is the non-regular graph structure, the initial information amount of each node is distributed according to the principle of univesral distribution, is also that the initial information amount of each node is identical, equals gross information content at the mean value of each node; If the structure of figure is the regular graph structure, when determining the initial information amount of each node, can select one of them node as start node, determine that then the initial information amount of this start node is 1, the initial information amount of other node is 0.The selection of this start node can be arbitrarily, and after determining the initial information amount of each node, information computing unit 1204 obtains each nodes not in the same time or the quantity of information of Preset Time point.Wherein, default time point has a plurality of, can set as required.For example, be first Preset Time point if set the initial time of node, the initial information amount that is also node is the nodal information amount under the first Preset Time point, the quantity of default time point can be identical with the quantity of node, can certainly be different.Information determining unit 1205 can be determined the structure descriptor of figure according to the time series of the quantity of information distribution of all nodes.

The embodiment of the present invention is passed through said units, realize the performance analysis to the structure of figure, and then obtained the dynamic descriptor of the structure of figure, thereby increased the quantity of information of the structure descriptor of figure, fully characterize the structure of figure, can more effectively describe the structure of figure.

Wherein, in one exemplary embodiment of the present invention, the figure converting unit can specifically be configured to the structure of described figure is converted to Markov figure, comprise the directed edge that each node in the structure of described figure points to oneself in described Markov figure, the corresponding directed edge of internodal line, and the weight of calculating each directed edge that obtains based on the original tally distribution of line between node and node; Described information flow rule is carried out information flow for the quantity of information of each node according to the weight of internodal directed edge and directed edge.

In another exemplary embodiment of the present invention, this information computing unit can comprise structure as shown in figure 13:

The first computation subunit 1301, the quantity of information that is configured to calculate respectively each node current preset time point flow to the product of weight of the directed edge of a certain node with it.

The second computation subunit 1302 is configured to sum product corresponding to each node that will obtain as the quantity of information of described a certain node at next Preset Time point.

In another exemplary embodiment of the present invention, the initial value determining unit specifically can be configured to when the structure of described figure is the non-regular graph structure, with the gross information content of described information flow cardon in the mean value of each node initial information amount as described each node.Determine the initial information amount of each node in this initial value determining unit after, the information determining unit specifically can comprise structure as shown in figure 14:

First obtains subelement 1401, is configured to obtain respectively the time series that each node distributes in the formed information of the quantity of information of all Preset Time points.

The first sequence subelement 1402 is configured to the time series that the information of each node distributes is sorted according to the first pre-defined rule.

First determines subelement 1403, is configured to the structure descriptor of the time series after sequence as described figure.

In another exemplary embodiment of the present invention, the initial value determining unit specifically can also comprise structure as shown in figure 15:

Chooser unit 1501 is configured to select start node in each node when the structure of described figure is the regular graph structure

Initial definite subelement 1502 is configured to the initial information amount of described start node in described information flow cardon is defined as 1, and the initial information amount of other node in described information flow cardon is defined as 0.

When the initial value determining unit according to above-mentioned chooser unit 1501, initially determine that subelement 1502 is determined the initial information amount of each nodes after, the information determining unit specifically can comprise structure as shown in figure 16:

Second obtains subelement 1601, is configured to respectively with different nodes to obtain start node and other node in the time series of the formed information distribution of the quantity of information of all Preset Time points as described start node

The second sequence subelement 1602 is configured to the time series that the information of each node corresponding to identical start node distributes is sorted according to the second pre-defined rule

Second determines subelement 1603, is configured to according to presetting selective rule, and the time series that the information of each node that the start node after selecting to sort is corresponding distributes is as the structure descriptor of described figure.

Realized performance analysis to the structure of figure by each unit of said apparatus in the embodiment of the present invention, and then obtained the dynamic descriptor of the structure of figure, thereby increased the quantity of information of the structure descriptor of figure, fully characterized the structure of figure, can more effectively describe the structure of figure.

In addition, should also be noted that above-mentioned series of processes and device also can be by software and/or hardware and/or firmware realizations.In the situation that realize by software and/or firmware, from storage medium or network to the computing machine with specialized hardware structure, for example general purpose personal computer 1700 shown in Figure 17 is installed the program that consists of this software, and this computing machine can be carried out various functions etc. when various program is installed.

In Figure 17, CPU (central processing unit) (CPU) 1701 carries out various processing according to the program of storage in ROM (read-only memory) (ROM) 1702 or from the program that storage area 1708 is loaded into random access memory (RAM) 1703.In RAM 1703, also store as required data required when CPU 1701 carries out various processing etc.

CPU 1701, ROM 1702 and RAM 1703 are connected to each other via bus 1704.Input/output interface 1705 also is connected to bus 1704.

Following parts are connected to input/output interface 1705: importation 1706 comprises keyboard, mouse etc.; Output 1707 comprises display, such as cathode-ray tube (CRT) (CRT), liquid crystal display (LCD) etc., and loudspeaker etc.; Storage area 1708 comprises hard disk etc.; With communications portion 1709, comprise that network interface unit is such as LAN card, modulator-demodular unit etc.Communications portion 1709 is processed such as the Internet executive communication via network.

As required, driver 1710 also is connected to input/output interface 1705.Detachable media 1711 makes the computer program of therefrom reading be installed to as required in storage area 1708 such as disk, CD, magneto-optic disk, semiconductor memory etc. are installed on driver 1710 as required.

In the situation that realize above-mentioned series of processes by software, from network such as the Internet or storage medium such as detachable media 1711 is installed the program that consists of softwares.

It will be understood by those of skill in the art that this storage medium is not limited to shown in Figure 17 wherein has program stored therein, distributes separately to provide the detachable media 1711 of program to the user with equipment.The example of detachable media 1711 comprises disk (comprising floppy disk (registered trademark)), CD (comprising compact disc read-only memory (CD-ROM) and digital universal disc (DVD)), magneto-optic disk (comprising mini-disk (MD) (registered trademark)) and semiconductor memory.Perhaps, storage medium can be hard disk that comprises in ROM 1702, storage area 1708 etc., computer program stored wherein, and be distributed to the user together with the equipment that comprises them.

The step that also it is pointed out that the above-mentioned series of processes of execution can order naturally following the instructions be carried out in chronological order, but does not need necessarily to carry out according to time sequencing.Some step can walk abreast or carry out independently of one another.

Said method of the present invention and device embodiment can be applied in various image processing field in acquisition process about the structure descriptor of figure, for example obtain the structure descriptor of figure in the electronic circuit from electronic applications after the extraction circuit structure, again for example, from various network architectures (as communication network, the internet, the network of communication lines), obtain the structure descriptor of figure after the extraction network data structure, obtain the structure descriptor of figure etc. after extraction molecular structure or DNA, RNA structure in chemical field or biological field.After the structure descriptor that obtains figure, just can carry out various subsequent treatment to figure by means of this descriptor, for example, relatively whether each figure is identical, etc.

About comprising the embodiment of above embodiment, following remarks is also disclosed:

1. 1 kinds of methods that obtain the structure descriptor of figure of remarks comprise:

Extract the structure of figure in the Given Graph picture;

The structure of described figure is converted to the information flow cardon, and described information flow cardon is in order to characterize the information flow space by the organization definition of described figure, and in the structure of described figure, each node carries out information flow according to the information flow rule in described information flow cardon;

Determine the initial information amount of each node in described information flow cardon in the structure of described figure;

According to the initial information amount of described each node, according to described information flow rule, calculate described each node in the quantity of information of Preset Time point;

According to by the time series of described each node in the formed information distribution of the quantity of information of all Preset Time points, determine the structure descriptor of described figure.

Remarks 2. method described according to remarks 1, wherein, described structure with described figure is converted to the information flow cardon, comprising:

The structure of described figure is converted to Markov figure, comprise the directed edge that each node in the structure of described figure points to oneself in described Markov figure, the corresponding directed edge of internodal line, and the weight of calculating each directed edge that obtains based on the original tally distribution of line between node and node;

Described information flow rule is carried out information flow for the quantity of information of each node according to the weight of internodal directed edge and directed edge.

Remarks 3. method described according to remarks 2, wherein, described initial information amount according to described each node according to described information flow rule, is calculated described each node in the quantity of information of Preset Time point, comprising:

The quantity of information of calculating respectively each node current preset time point flows to the product of weight of the directed edge of a certain node with it;

The sum product that each node of obtaining is corresponding is as the quantity of information of described a certain node at next Preset Time point.

Remarks 4. is according to the described method of any one in remarks 1 to 3, and wherein, the initial information amount of each node in described information flow cardon in the structure of described definite described figure comprises:

When the structure of described figure is the non-regular graph structure, with the gross information content of described information flow cardon in the mean value of each node initial information amount as described each node.

Remarks 5. method described according to remarks 4, wherein, described basis in the time series that the formed information of the quantity of information of all Preset Time points distributes, is determined the structure descriptor of described figure by described each node, comprising:

Obtain respectively the time series that each node distributes in the formed information of the quantity of information of all Preset Time points;

The time series that the information of each node is distributed sorts according to the first pre-defined rule;

With the structure descriptor of the time series after sequence as described figure.

Remarks 6. is according to the described method of any one in remarks 1 to 3, and wherein, the initial information amount of each node in described information flow cardon in the structure of described definite described figure comprises:

When the structure of described figure is the regular graph structure, select start node in each node;

The initial information amount of described start node in described information flow cardon is defined as 1, and the initial information amount of other node in described information flow cardon is defined as 0.

Remarks 7. method described according to remarks 6, wherein, described basis in the time series that the formed information of the quantity of information of all Preset Time points distributes, is determined the structure descriptor of described figure by described each node, comprising:

Respectively with different nodes as described start node, obtain start node and other node in the time series of the formed information distribution of the quantity of information of all Preset Time points;

The time series that the information of each node corresponding to identical start node is distributed sorts according to the second pre-defined rule;

According to presetting selective rule, the time series that the information of each node that the start node after selecting to sort is corresponding distributes is as the structure descriptor of described figure.

Remarks 8. method described according to remarks 1, wherein, the quantity of described Preset Time point equals the quantity of node in the structure of described figure at least.

9. 1 kinds of graph structure control methodss of remarks comprise:

According to the described method of any one in above-mentioned remarks 1 to 8, obtain the structure descriptor of at least two figure;

If the structure descriptor of described at least two figure is identical, the structure of described at least two figure is identical.

10. 1 kinds of devices that obtain the structure descriptor of figure of remarks comprise:

The structure extraction unit is configured to the structure of extraction figure in the Given Graph picture;

The figure converting unit, be configured to the structure of described figure is converted to the information flow cardon, described information flow cardon is in order to characterize the information flow space by the organization definition of described figure, and in the structure of described figure, each node carries out information flow according to the information flow rule in described information flow cardon;

The initial value determining unit is configured to determine the initial information amount of each node in described information flow cardon in the structure of described figure;

The information computing unit is configured to the initial information amount according to described each node, according to described information flow rule, calculates described each node in the quantity of information of Preset Time point;

The information determining unit is configured to determine the structure descriptor of described figure according to by the time series of described each node in the formed information distribution of the quantity of information of all Preset Time points.

Remarks 11. device described according to remarks 10, wherein,

Described figure converting unit, specifically be configured to the structure of described figure is converted to Markov figure, comprise the directed edge that each node in the structure of described figure points to oneself in described Markov figure, the corresponding directed edge of internodal line, and the weight of calculating each directed edge that obtains based on the original tally distribution of line between node and node; Described information flow rule is carried out information flow for the quantity of information of each node according to the weight of internodal directed edge and directed edge.

Remarks 12. device described according to remarks 11, wherein, described information computing unit comprises:

The first computation subunit, the quantity of information that is configured to calculate respectively each node current preset time point flow to the product of weight of the directed edge of a certain node with it;

The second computation subunit is configured to sum product corresponding to each node that will obtain as the quantity of information of described a certain node at next Preset Time point.

Remarks 13. is according to the described device of any one in remarks 10 to 12, wherein,

Described initial value determining unit specifically is configured to when the structure of described figure is the non-regular graph structure, with the gross information content of described information flow cardon in the mean value of each node initial information amount as described each node.

Remarks 14. device described according to remarks 13, wherein, described information determining unit comprises:

First obtains subelement, is configured to obtain respectively the time series that each node distributes in the formed information of the quantity of information of all Preset Time points;

The first sequence subelement is configured to the time series that the information of each node distributes is sorted according to the first pre-defined rule;

First determines subelement, is configured to the structure descriptor of the time series after sequence as described figure.

Remarks 15. is according to the described device of any one in remarks 10 to 12, and wherein, described initial value determining unit comprises:

The chooser unit is configured to select start node in each node when the structure of described figure is the regular graph structure;

Initial definite subelement is configured to the initial information amount of described start node in described information flow cardon is defined as 1, and the initial information amount of other node in described information flow cardon is defined as 0.

Remarks 16. device described according to remarks 15, wherein, described information determining unit comprises:

Second obtains subelement, is configured to respectively with different nodes to obtain start node and other node in the time series of the formed information distribution of the quantity of information of all Preset Time points as described start node;

The second sequence subelement is configured to the time series that the information of each node corresponding to identical start node distributes is sorted according to the second pre-defined rule;

Second determines subelement, is configured to according to presetting selective rule, and the time series that the information of each node that the start node after selecting to sort is corresponding distributes is as the structure descriptor of described figure.

Although described the present invention and advantage thereof in detail, be to be understood that in the situation that do not break away from the spirit and scope of the present invention that limited by appended claim and can carry out various changes, alternative and conversion.And, the term of the embodiment of the present invention " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby make the process, method, article or the equipment that comprise a series of key elements not only comprise those key elements, but also comprise other key elements of clearly not listing, or also be included as the intrinsic key element of this process, method, article or equipment.In the situation that not more restrictions, the key element that is limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment that comprises described key element and also have other identical element.

Claims (10)

1. method that obtains the structure descriptor of figure comprises:

Extract the structure of figure in the Given Graph picture;

The structure of described figure is converted to the information flow cardon, and described information flow cardon is in order to characterize the information flow space by the organization definition of described figure, and in the structure of described figure, each node carries out information flow according to the information flow rule in described information flow cardon;

Determine the initial information amount of each node in described information flow cardon in the structure of described figure;

According to the initial information amount of described each node, according to described information flow rule, calculate described each node in the quantity of information of Preset Time point;

According to by the time series of described each node in the formed information distribution of the quantity of information of all Preset Time points, determine the structure descriptor of described figure.

2. method according to claim 1, wherein, described structure with described figure is converted to the information flow cardon, comprising:

The structure of described figure is converted to Markov figure, comprise the directed edge that each node in the structure of described figure points to oneself in described Markov figure, the corresponding directed edge of internodal line, and the weight of calculating each directed edge that obtains based on the original tally distribution of line between node and node;

Described information flow rule is carried out information flow for the quantity of information of each node according to the weight of internodal directed edge and directed edge.

3. method according to claim 2, wherein, described initial information amount according to described each node is calculated described each node in the quantity of information of Preset Time point according to described information flow rule, comprising:

The quantity of information of calculating respectively each node current preset time point flows to the product of weight of the directed edge of a certain node with it;

The sum product that each node of obtaining is corresponding is as the quantity of information of described a certain node at next Preset Time point.

4. the described method of any one according to claim 1 to 3, wherein, the initial information amount of each node in described information flow cardon in the described structure of determining described figure comprises:

When the structure of described figure is the non-regular graph structure, with the gross information content of described information flow cardon in the mean value of each node initial information amount as described each node.

5. method according to claim 4, wherein, described basis in the time series that the formed information of the quantity of information of all Preset Time points distributes, is determined the structure descriptor of described figure by described each node, comprising:

Obtain respectively the time series that each node distributes in the formed information of the quantity of information of all Preset Time points;

The time series that the information of each node is distributed sorts according to the first pre-defined rule; With the structure descriptor of the time series after sequence as described figure.

6. the described method of any one according to claim 1 to 3, wherein, the initial information amount of each node in described information flow cardon in the described structure of determining described figure comprises:

When the structure of described figure is the regular graph structure, select start node in each node;

The initial information amount of described start node in described information flow cardon is defined as 1, and the initial information amount of other node in described information flow cardon is defined as 0.

7. method according to claim 6, wherein, described basis in the time series that the formed information of the quantity of information of all Preset Time points distributes, is determined the structure descriptor of described figure by described each node, comprising:

Respectively with different nodes as described start node, obtain start node and other node in the time series of the formed information distribution of the quantity of information of all Preset Time points;

The time series that the information of each node corresponding to identical start node is distributed sorts according to the second pre-defined rule;

According to presetting selective rule, the time series that the information of each node that a certain start node after selecting to sort is corresponding distributes is as the structure descriptor of described figure.

8. method according to claim 1, wherein, the quantity of described Preset Time point equals the quantity of node in the structure of described figure at least.

9. graph structure control methods comprises:

According to the described method of any one in the claims 1 to 8, obtain the structure descriptor of at least two figure to be contrasted;

If the structure descriptor of described at least two figure is identical, the structure of at least two figure described to be contrasted is identical.

10. device that obtains the structure descriptor of figure comprises:

The structure extraction unit is configured to the structure of extraction figure in the Given Graph picture;

The figure converting unit, be configured to the structure of described figure is converted to the information flow cardon, described information flow cardon is in order to characterize the information flow space by the organization definition of described figure, and in the structure of described figure, each node carries out information flow according to the information flow rule in described information flow cardon;

The initial value determining unit is configured to determine the initial information amount of each node in described information flow cardon in the structure of described figure;

The information computing unit is configured to the initial information amount according to described each node, according to described information flow rule, calculates described each node in the quantity of information of Preset Time point;

The information determining unit is configured to determine the structure descriptor of described figure according to by the time series of described each node in the formed information distribution of the quantity of information of all Preset Time points.

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