CN110211126A - Image partition method based on intuitionistic fuzzy C mean cluster - Google Patents

Image partition method based on intuitionistic fuzzy C mean cluster Download PDF

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CN110211126A
CN110211126A CN201910507275.7A CN201910507275A CN110211126A CN 110211126 A CN110211126 A CN 110211126A CN 201910507275 A CN201910507275 A CN 201910507275A CN 110211126 A CN110211126 A CN 110211126A
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intuitionistic fuzzy
cluster centre
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赵凤
郝浩
刘汉强
范九伦
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Xian University of Posts and Telecommunications
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Abstract

The invention discloses a kind of image partition methods based on intuitionistic fuzzy C mean cluster.Mainly solve the problems, such as in image segmentation to noise-sensitive, be easily trapped into local optimum, clusters number needs to set, scheme is: using based on switch mean value constructing tactics to the intuitionistic Fuzzy Sets of noise robustnessIt carries out curve fitting to the grey value profile of image, screens all peak points of matched curve as cluster centre initial value range set, and count maximum cluster centre quantity；On the basis of, using the linear weighting function coefficient in location information and grayscale information the construction intuitionistic fuzzy objective function of pixel, obtain subordinated-degree matrix U；U is evaluated according to classification range index evaluation index, obtains optimal subordinated-degree matrix, and be used for error detection Policy Filtering mistake point pixel and correctly classified.Invention enhances the robustness to noise, can adaptively determine image clustering number, can be used for the pretreatment of image recognition and computer vision.

Description

Image partition method based on intuitionistic fuzzy C mean cluster
Technical field
The invention belongs to digital image processing fields, and in particular to arrive a kind of image partition method, can be used for image recognition With the pretreatment of computer vision.
Background technique
From eighties of last century the seventies and eighties, numerous scholars give more sustained attention image segmentation, and image Segmentation Technology has become The basic technology of various fields, as long as concerning image Segmentation Technology, image point cannot be lacked the problem of extracting content in image The quality and effect for cutting work will directly or indirectly influence subsequent Image Engineering.Existing image partition method type is numerous It is more, the image partition method based on threshold value, the image partition method based on edge, the image segmentation based on region can be summarized as Method, image partition method based on cluster etc., conventional images cutting techniques are with the image partition method based on threshold value and are based on Based on the image partition method of cluster, the image segmentation based on threshold value is a kind of method divided using demographic information, usually Suitable threshold value is extracted, target part interested in image is separated.But its to be only applicable to gray difference apparent Image, in addition, the interference elements such as noise also cause difficulty to the Accurate Segmentation work of image.Up to the present it is not formed still A kind of unified theoretical system is applied to different fields, and image segmentation remains a popular research field.
Image is divided into according to the similitude between pixel in image by multiple classes based on the image partition method of cluster Not, good with segmentation effect, time-consuming short, the features such as method is stable, becomes apply universal one of method in recent years.Often Cluster segmentation method has: K mean value, Fuzzy C-Means Clustering, hierarchical cluster and spectral clustering) etc., wherein Fuzzy C-means Cluster is one of the method being most widely used.Fuzzy C-mean algorithm method proposes by Dunne earliest, referring to: Dunne is a kind of and iteration The relevant fuzzy image processing method and its application of Self Organization Analysis algorithm are in the compact easily separated cluster cybernetics journal of detection .1973,3(3):32-57.(J.C.Dunn.A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters[J].Journal of Cybernetics, 1973,3 (3): 32-57.), this method is not due to accounting for any spatial information in image, to the noise-sensitive in image, So that segmentation noisy image cannot get ideal segmentation effect.Later many scholars have delivered to be changed in conjunction with image space neighborhood information Into method.Ahmed et al. space neighborhood information item is added in the objective function of fuzzy C-mean algorithm, proposes FCM_S algorithm, Although the algorithm improves the robustness to noise, but computation complexity is higher, in order to which the calculating for reducing FCM_S algorithm is complicated Neighborhood information is introduced into the objective function of algorithm by degree, Chen Songcan and Zhang Daoqiang by way of mean filter and median filtering In, FCM_S1 and FCM_S2 algorithm is proposed, referring to: a kind of stable combination space based on kernel function Chen Songcan, Zhang Daoqiang Volumes 34,1907 of transactions of information fuzzy C mean value image segmentation algorithm American Electronic electrical engineers' association system control processing- 1916,2004.(S.Chen and D.Zhang,“Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-induced Distance Measure,”IEEE Trans.Syst,Man,Cybern,vol.34,pp1907-1916,2004.)；FCM_S1 algorithm is to Gauss in both algorithms Noise processed effect is preferable, to salt-pepper noise poor robustness, though two FCM_S2 algorithms are preferable to salt-pepper noise treatment effect, but But to Gaussian noise poor robustness, thus both algorithms cannot accomplish the robust universality to multiple types noise.Cai Weiling Et al. combine image spatial information and grayscale information construct linear weighted function and image, propose and quickly generate FCM algorithm, join See: a kind of fuzzy C-mean algorithm of the fast robust of the introducing local message for image segmentation of Cai Weiling, Chen Songcan, Zhang Daoqiang is poly- Volumes of 40,825-838,2007. of class algorithm pattern-recognition (W.Cai, S.Chen, and D.Zhang, " Fast and Robust Fuzzy C-means Clustering Algorithms Incorporating Local Information for Image Segmentation, " Pattern Recognit., vol.40, no.3, pp.825-838, Mar.2007.), this method is right Gaussian noise has and also has preferable robustness while higher robustness to salt-pepper noise；But algorithm above does not all account for The more ambiguities of data, Cha Erre et al. is it has further been discovered that can be considered the more moulds of data using intuitionistic Fuzzy Sets theory Paste property, it is more accurate to the classification of data, the fuzzy clustering method based on intuitionistic fuzzy data is proposed, referring to: a kind of novelty Intuitionistic fuzzy C means clustering algorithm and its in medical image apply application soft com-puting (T, Chaira. " A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images."Appl.Soft Comput.11(2):1711-1717,2011.)；Due to being based on intuitionistic fuzzy data Fuzzy clustering method it is also more sensitive to noise, Wei Erma et al. further by local spatial information introduce intuitionistic fuzzy C it is equal In value-based algorithm, referring to: Wei Erma, Agrawal, improvement of the Sha Lan for the combination local spatial information of brain image segmentation Intuitionistic fuzzy C mean algorithm application soft com-puting .543-557,2016. (H.Verma, R.K.Agrawal, A.Sharan, " An Improved Intuitionistic Fuzzy C-means Clustering Algorithm Incorporating Local Information for Brain Image Segmentation,”Appl.Soft Comput.,543–557, 2016)。
Although above-mentioned improved method optimizes performance of the fuzzy clustering algorithm on noiseproof feature to a certain extent, still It is very sensitive to the initial value of cluster centre in the presence of not strong enough to noise robustness, it is unable to adaptive analysis image clustering number etc. It is insufficient.
Summary of the invention
It is a kind of based on intuitionistic fuzzy C mean cluster it is an object of the invention in view of the above shortcomings of the prior art, provide Image partition method improve the accuracy of separation to enhance to the robustness of noise, reduce the sensitivity to cluster centre initial value Property, adaptively determine image clustering number.
To achieve the above object, technology of the invention includes the following steps:
(1) image to be split is inputted, if image to be split is color image, is first converted into gray level image；
(2) maximum number of iterations T=100, outage threshold ε=10 initiation parameter: are set-5, Fuzzy Weighting Exponent m=2, neighbour Domain window radius ω=3, primary iteration number t=1, clusters number default initial values Y=2；
(3) using the intuitionistic Fuzzy Sets based on switch mean value constructing tactics to noise robustnessWherein, 1≤i≤n, n table Show sum of all pixels mesh；
(4) it is directed to the grey value profile of input picture, is carried out curve fitting using least square method, selects matched curve Cluster centre value of the gray level corresponding to peak point as initialization, and the range of estimation cluster numbers is set as { 2 ..., Ymax, Cluster centre initial value range set C isWherein, cYIndicate the initial of the Y cluster centre Value, 1≤Y≤Ymax, YmaxIndicate the maximum value of clusters number, 2≤Ymax≤ n, T indicate transposition；
(5) linear weighting function coefficient H is iterated to calculateir, Intuitionistic Fuzzy DistancesWith intuitionistic fuzzy objective function J:
(5a) constructs linear weighting function coefficient H using the location information and grayscale information of pixelir:
Wherein, (ai, bi) and (ar, br) respectively indicate the spatial value of center pixel i and neighborhood territory pixel r, xiAnd xrPoint Not Biao Shi center pixel i and neighborhood territory pixel r gray value, ukiIndicate xiFor k-th of cluster centre ckDegree of membership, 1≤k≤ Y, 1≤r≤ω2；
(5b) calculates pixel x under intuitionistic Fuzzy SetsiTo cluster centre ckIntuitionistic Fuzzy DistancesWherein,WithRespectively indicate pixel xiWith cluster centre ckIntuitionistic Fuzzy Sets；
The linear weighting function coefficient H that (5c) is obtained in conjunction with (5a)irThe Intuitionistic Fuzzy Distances obtained with (5b) Intuitionistic fuzzy objective function J is calculated, and solves the subordinating degree function u in intuitionistic fuzzy objective function JkiWith cluster centre ckIt is straight Feel fuzzy set
(6) judge iteration stopping condition | | C(t+1)-C(t)| | whether < ε or operation the number of iterations t > T are true:
If invalid, t=t+1 is enabled, is returned (5)；
If set up, exports subordinated-degree matrix U and judge Y < YmaxIt is whether true, if so, Y=Y+1 is then enabled, cluster is taken The preceding Y numerical value of center initial value range set C, enables the initial value set of cluster centre bePrimary iteration number t=1 is enabled, is returned (5), if Y < YmaxIt is invalid, it executes (7), Wherein,For 0 cluster centre numerical value set,Indicate the Q cluster centre iteration The t times intuitionistic Fuzzy Sets,Indicate the initial value of the P cluster centre, 1≤Q≤Y, 1≤P≤Y, U={ uki}；
(7) (Y is inputted from (6)max- 1) a subordinated-degree matrix U, and these are evaluated using classification range index evaluation index GD Subordinated-degree matrix；If the maximum subordinated-degree matrix of GD numerical value is Umax, by UmaxCorresponding clusters number YfinAs input picture Final clusters number, wherein 1≤Yfin≤Ymax；
(8) to the subordinated-degree matrix U of (7) outputmaxPixel classifications are done, and carry out error detection classification, export segmented image:
(8a) is according to maximum membership grade principle and final clusters number Yfin, to the subordinated-degree matrix U of (7) outputmaxPicture Element is classified, output label matrix UL, wherein ULInside contain only categorization values { 1,2 ..., YW..., Yfin, 1≤YW≤Yfin；
(8b) sets the label class label of center pixel i as Li, multiply in 5 neighborhoods the 5 of center pixel i, judge label classification Value LiPixel quantity nLWhether n is metL≤25/(Yfin+ 1) condition:
If it is satisfied, then determining that center pixel i divides pixel for mistake, and mistake is divided to the label class label L of pixeliIt is modified to neighbour The most label class label of label class label quantity of the same race in domain executes (8c)；
If conditions are not met, then determining that center pixel i is correct distribution pixel, execute (8c)；
(8c) judges label matrix ULIn pixel whether all traverse, if so, exporting final label matrix UF, execute (8d) continues to traverse if it is not, returning to (8b)；
(8d) is to final label matrix UFMiddle label class label is YWPixel assign different corresponding gray value xW, and will The matrix for assigning different gray values is exported as segmented image.
Compared with prior art, the invention has the following beneficial technical effects:
First, the present invention is used based on switch mean value constructing tactics to the intuitionistic Fuzzy Sets of noise robustness, utilizes pixel Location information and grayscale information construction linear weighting function coefficient are simultaneously introduced into intuitionistic fuzzy objective function, are solved cluster and are calculated The problem of method is to noise-sensitive.
Second, the present invention is carried out curve fitting using grey value profile of the least square method to image, and selects fitting bent Gray level corresponding to all peak points of line is commented as cluster centre initial value range according to classification range index evaluation index Valence subordinated-degree matrix selects the clusters number for being suitble to input picture, realizes adaptively determining for image clustering number, and overcome The shortcomings that traditional clustering algorithm is easily trapped into local optimum.
Third, the present invention divides pixel using error detection Policy Filtering mistake, and divides element correctly to be distributed mistake, improves point Accuracy is cut, so that segmentation effect is even more ideal.
Detailed description of the invention
Fig. 1 is implementation flow chart of the invention；
Fig. 2 is to carry out emulation segmentation to the #3096 image in Berkeley image data base with the present invention and existing method Comparative result figure；
Fig. 3 is to carry out emulation point to the #238011 image in Berkeley image data base with the present invention and existing method The comparative result figure cut.
Specific embodiment
The embodiment of the present invention and effect are further described below in conjunction with attached drawing:
Referring to Fig. 1, steps are as follows for realization of the invention:
Step 1: inputting image to be split.
Image to be split is inputted, if image to be split is color image, is first converted into gray level image.
Step 2: setting initial parameter value.
If maximum number of iterations T=100, outage threshold ε=10-5, Fuzzy Weighting Exponent m=2, neighborhood window radius ω=3, Primary iteration number t=1, clusters number default initial values Y=2.
Step 3: constructing the intuitionistic Fuzzy Sets to noise robustness
The method of prior art configuration intuitionistic Fuzzy Sets includes IFCM algorithm and IIFCM algorithm, is calculated using Yager Son constructs intuitionistic Fuzzy Sets.
This example use based on switch mean value strategy amendment input picture grey scale pixel value, and by Yager operator come Construct the intuitionistic Fuzzy Sets to noise robustness, wherein switch mean value strategy is according to the pixel grey scale Distribution value in pixel i neighborhood Situation does gray value amendment, is accomplished by
3.1) first determine whether the grey scale pixel value multiplied in 3 neighborhoods the 3 of center pixel i is not all 0 or 255:
If set up, then it represents that there are also other gray values in addition to 0 or 255 for the grey scale pixel value in neighborhood, first by neighborhood The pixel that interior gray value is 0 and 255 is taken out, then takes the gray average of rest of pixels in neighborhood, and be set as the amendment of center pixel Numerical value, then execute 3.2)；
If invalid, the gray value of center pixel is not modified, is directly executed 3.2)；
3.2) judge whether the pixel in input picture all traverses: if so, the revised image of output gray level value, holds Row 3.3), 3.1) continue to traverse if it is not, returning；
3.3) 3.2) grey scale pixel value of the amendment image of output is calculated as intuitionistic Fuzzy Sets
3.3.1 μ (x) is seti) indicate grey scale pixel value xiDegree of membership, v (xi) and π (xi) it is x respectivelyiUse Yager operator The non-affiliated degree and hesitation degree of construction, μ (xi)、v(xi) and π (xi) calculation formula difference it is as follows:
π(xi)=1- μ (xi)-v(xi),
Wherein, 0≤μ (xi)≤1,0≤v (xi)≤1,0≤μ (xi)+v(xi)≤1, α=0.8 indicates Yager operator system Number；
3.3.2) according to 3.3.1) μ (x that is calculatedi)、v(xi) and π (xi) construction intuitionistic Fuzzy Sets
Step 4: for the grey value profile of input picture, being carried out curve fitting using least square method, selection fitting is bent Cluster centre value of the gray level corresponding to the peak point of line as initialization estimates that maximum clusters number and cluster centre are initial It is worth range.
The existing technology for choosing cluster centre initial value has the peak point for directly choosing image grey level histogram as initial Cluster centre.
This example is first carried out curve fitting to image grayscale Distribution value using least square method, then bent in smooth fitting Line options peak point, is accomplished by
4.1) gray level image is set as 256 gray levels, grey level range is [0,255], and draws the ash of gray level image Spend histogram；
4.2) number of pixels corresponding to 256 gray levels on grey level histogram is counted, grey value profile frequency f is set as (l), curve matching and using seven rank least square methods is done to f (l), gray value data point is fitted to gray value curve, wherein 0≤l≤255；
4.3) the first derivative f ' (l) of f (l) is sought, and compares the first derivative f ' (l- of gray level before and after f ' (l) and f (l) And f ' (l+1) 1)；
4.4) judge gray level l whether under conditions of l=0 l=255:
It, will if set up if under conditions of l=0 l=255 and l=0, then judging whether f ' (l) < 0 is true Gray level l=0 is exported as peak point, then is executed 4.5)；
If under conditions of l=0 l=255 and l=255, then judging whether f ' (l) > 0 is true, if set up, It exports, then is executed 4.5) using gray level l=255 as peak point；
If not indicating under conditions of 0 255 < < l under conditions of l=0 l=255, then judging f ' (l) Whether=0 and f ' (l-1) > 0 and f ' (l+1) < 0 are true, if set up, export gray level l as peak point, then execute 4.5)；
4.5) judge whether 256 gray levels all traverse, if so, 4.6) output peak point, executes, if it is not, 4.4) return continues to traverse；
4.6) it sorts to the peak point of 4.5) output according to the sequence of intensity profile frequency values from big to small, counts peak point Number be YmaxIf cluster numbers range is 2 to YmaxIt is a, and the corresponding gray level l of peak point sorted from large to small is counted, it obtains It is to gray scale value rangeIt is cluster centre initial value that gray level, which is corresponded to numerical statistic, i.e.,If cluster centre initial value range set C isWherein:
lYIndicate that in peak point intensity profile frequency values, the Y peak point intensity profile is frequently according in descending order Gray level corresponding to rate value, cYIndicate the initial value of the Y cluster centre, 1≤Y≤Ymax, YmaxIndicate clusters number most Big value, 2≤Ymax≤ n, T indicate transposition.
Step 5: iterative calculation linear weighting function coefficient Hir, Intuitionistic Fuzzy DistancesWith intuitionistic fuzzy target letter Number J.
The local spatial information of image is introduced into its objective function by existing FCM_S1 algorithm and FCM_S2 algorithm, In, what FCM_S1 algorithm considered is the mean filter feature of image, and what FCM_S2 algorithm considered is the median filtering feature of image.
This example constructs linear weighting function coefficient H first with the location information and grayscale information of pixelir, then will include The H of image local spatial informationirIt is introduced into intuitionistic fuzzy objective function J, is accomplished by
5.1) linear weighting function coefficient H is constructed using the location information of pixel and grayscale informationir:
Wherein, (ai,bi) and (ar,br) respectively indicate the spatial value of center pixel i and neighborhood territory pixel r, xiAnd xrPoint Not Biao Shi center pixel i and neighborhood territory pixel r gray value, ukiIndicate xiFor k-th of cluster centre ckDegree of membership, 1≤k≤ Y, 1≤r≤ω2；
5.2) grey scale pixel value x under intuitionistic Fuzzy Sets is calculatediTo cluster centre ckIntuitionistic Fuzzy Distances
5.2.1 μ (c) is setk) indicate cluster centre ckDegree of membership, v (ck) and π (ck) respectively indicate cluster centre ckIt uses The non-affiliated degree and hesitation degree of Yager operator construction, μ (ck)、v(ck) and π (ck) calculation formula difference it is as follows:
π(ck)=1- μ (ck)-v(ck),
Wherein, 0≤μ (ck)≤1,0≤v (ck)≤1,0≤μ (ck)+v(ck)≤1；
5.2.2 Intuitionistic Fuzzy Distances) are calculated according to the result that (5.2.1) is obtained
Wherein, μ (xi)、v(xi) and π (xi) respectively indicate grey scale pixel value xiDegree of membership, non-affiliated degree and hesitation degree；
5.3) the linear weighting function coefficient H obtained in conjunction with (5.1)irThe Intuitionistic Fuzzy Distances obtained with (5.2)Calculate intuitionistic fuzzy objective function J:
Wherein, ukiIt is pixel xiTo cluster centre ckSubordinating degree function, m indicate FUZZY WEIGHTED index；
5.4) intuitionistic fuzzy objective function J is solved using lagrange's method of multipliers, and obtains subordinating degree function ukiAnd cluster Center ckIntuitionistic Fuzzy Sets
5.4.1 Lagrange multiplier λ) is introduced in intuitionistic fuzzy objective function Ji, constructing has Lagrange multiplier Objective function L:
5.4.2 the objective function L with Lagrange multiplier) is calculated separately for subordinating degree function uki, cluster centre ck Intuitionistic Fuzzy SetsWith Lagrange multiplier λiPartial derivativeWith
5.4.3) in partial derivativeUnder conditions of, by acquiring subordinating degree function u as followskiWith cluster Center ckIntuitionistic Fuzzy Sets
Wherein, k=1,2 ..., Y, i=1,2 ..., n, r=1,2 ..., ω2。
Step 6: judging whether to meet iteration stopping condition.
If meeting iteration stopping condition | | C(t+1)-C(t)| | < ε or operation the number of iterations t > T then exports degree of membership square Battle array U, and execute 7), whereinFor cluster centre numerical value set,Indicate Q The intuitionistic Fuzzy Sets that cluster centre iteration is the t times；
If being unsatisfactory for iteration stopping condition, t=t+1 is enabled, is returned 5.1).
Step 7: judging whether current clusters number Y meets condition.
If meeting current clusters number Y is less than maximum clusters number Ymax, i.e. Y < Ymax, then Y=Y+1 is enabled, is then taken poly- The preceding Y numerical value of class center initial value range set C, is output to the initial value set of cluster centre, and enable cluster centre initial value Set C(1)ForPrimary iteration number t=1 is enabled, is returned 5.1), whereinIndicate P The initial value of a cluster centre, 1≤P≤Y；
If being unsatisfactory for Y < Ymax, then (8) are executed.
Step 8: evaluating subordinated-degree matrix using classification range index evaluation index GD.
The existing evaluation index to subordinated-degree matrix has division factor VpcWith partition entropy Vpe, can reflect degree of membership square The fog-level of battle array, wherein VpcNumerical value is bigger, and the ambiguity of subordinated-degree matrix is smaller, VpeNumerical value is smaller, and pixel classifications are more quasi- Really.
This example evaluates subordinated-degree matrix using classification range index evaluation index GD, i.e., inputs (Y from 7) middlemax- 1) a Subordinated-degree matrix U evaluates these subordinated-degree matrix using classification range index evaluation index GD, and GD numerical value is maximum is subordinate to for output Matrix is spent, and sets the maximum subordinated-degree matrix of GD numerical value as Umax, by UmaxCorresponding clusters number YfinMost as input picture Whole clusters number, in which: classification range index evaluation index GD formula is as follows:
Wherein, ui1It indicates that ith pixel is maximum and is subordinate to angle value, ui2Indicate ith pixel it is second largest be subordinate to angle value, 1 ≤Yfin≤Ymax。
Step 9: to subordinated-degree matrix UmaxCarry out pixel classifications.
According to maximum membership grade principle and final clusters number Yfin, to the subordinated-degree matrix U of 8) outputmaxPixel carry out Classification, output label matrix UL, wherein maximum membership grade principle is according to Vk=argk{max(uki) pixel i is divided into person in servitude The maximum classification V of category degreek, ULInside contain only categorization values { 1,2 ..., YW..., Yfin, 1≤YW≤Yfin。
Step 10: to label matrix ULCarry out error detection classification.
10.1) the label matrix U of 9) output is setLCenter pixel i label class label be Li, 5 in center pixel i multiply 5 In neighborhood, label class label L is judgediPixel quantity nLWhether n is metL≤25/(Yfin+ 1) condition:
If it is satisfied, then determining that center pixel i divides pixel for mistake, and mistake is divided to the label class label L of pixeljIt is modified to neighbour 10.2) the most label class label of label class label quantity of the same race in domain executes；
If conditions are not met, then determining that center pixel i is correct distribution pixel, execute 10.2)；
10.2) judge label matrix ULIn pixel whether all traverse, if so, exporting final label matrix UF, execute 11), 10.1) continue to traverse if it is not, returning.
Step 11: to final label matrix UFAssignment simultaneously exports image.
To the final label matrix U of 10.2) outputFMiddle label class label is YWPixel assign different corresponding gray values xW, and the matrix for assigning different gray values is exported as segmented image, final label matrix UFInside contain only categorization values 1, 2 ..., YW..., Yfin, assignment formula is as follows:
xW=[255/YW],
Wherein, [] indicates to take internal integer.
Below in conjunction with emulation experiment, technical effect of the invention is described further；
1. simulated conditions:
Emulation experiment is in computer Inter (R) Core (TM) i5-3210M 2.50GHZ CPU, 8G memory, MATLAB It is carried out under R2014a software environment.
2. emulation content:
The image that the number in Berkeley image data base is 3096 is chosen in emulation 1, with the present invention and the existing side FCM Method, FCM_S1 method, FCM_S2 method, FGFCM method, IFCM method and IIFCM method are respectively split it, as a result such as Shown in Fig. 2, in which:
2 (a) be the original image of 3096 images；
2 (b) be the Standard Segmentation figure of 3096 images；
2 (c) be the Gauss noisy image of 3096 images, noise intensity 0.006；
2 (d) be the segmentation result with existing FCM method to the Gauss noisy image of 3096 images；
2 (e) be the segmentation result with existing FCM_S1 method to the Gauss noisy image of 3096 images；
2 (f) be the segmentation result with existing FCM_S2 method to the Gauss noisy image of 3096 images；
2 (g) be the segmentation result with existing FGFCM method to the Gauss noisy image of 3096 images；
2 (h) be the segmentation result with existing IFCM method to the Gauss noisy image of 3096 images；
2 (i) be the segmentation result with existing IIFCM method to the Gauss noisy image of 3096 images；
2 (j) be the segmentation result with the present invention to the Gauss noisy image of 3096 images；
2 (k) be the spiced salt noisy image of 3096 images, noise intensity 0.01；
2 (l) be the segmentation result with existing FCM method to the spiced salt noisy image of 3096 images；
2 (m) be the segmentation result with existing FCM_S1 method to the spiced salt noisy image of 3096 images；
2 (n) be the segmentation result with existing FCM_S2 method to the spiced salt noisy image of 3096 images；
2 (o) be the segmentation result with existing FGFCM method to the spiced salt noisy image of 3096 images；
2 (p) be the segmentation result with existing IFCM method to the spiced salt noisy image of 3096 images；
2 (q) be the segmentation result with existing IIFCM method to the spiced salt noisy image of 3096 images；
2 (r) be the segmentation result with the present invention to the spiced salt noisy image of 3096 images；
Figure it is seen that the present invention can separate background and target on the basis of inhibiting noise, image is poly- Class number can be determined adaptively, insensitive to the initial value of cluster centre, so segmentation effect of the present invention to noisy image Better than existing FCM method, FCM_S1 method, FCM_S2 method, FGFCM method, IFCM method and IIFCM method.
The image that the number in Berkeley image data base is 238011 is chosen in emulation 2, with the present invention and existing FCM Method, FCM_S1 method, FCM_S2 method, FGFCM method, IFCM method and IIFCM method are respectively split it, as a result As shown in Figure 3, in which:
3 (a) be the original image of 238011 images；
3 (b) be the Standard Segmentation figure of 238011 images；
3 (c) be the Gauss noisy image of 238011 images, noise intensity 0.006；
3 (d) be the segmentation result with existing FCM method to the Gauss noisy image of 238011 images；
3 (e) be the segmentation result with existing FCM_S1 method to the Gauss noisy image of 238011 images；
3 (f) be the segmentation result with existing FCM_S2 method to the Gauss noisy image of 238011 images；
3 (g) be the segmentation result with existing FGFCM method to the Gauss noisy image of 238011 images；
3 (h) be the segmentation result with existing IFCM method to the Gauss noisy image of 238011 images；
3 (i) be the segmentation result with existing IIFCM method to the Gauss noisy image of 238011 images；
3 (j) be the segmentation result with the present invention to the Gauss noisy image of 238011 images；
3 (k) be the spiced salt noisy image of 238011 images, noise intensity 0.01；
3 (l) be the segmentation result with existing FCM method to the spiced salt noisy image of 238011 images；
3 (m) be the segmentation result with existing FCM_S1 method to the spiced salt noisy image of 238011 images；
3 (n) be the segmentation result with existing FCM_S2 method to the spiced salt noisy image of 238011 images；
3 (o) be the segmentation result with existing FGFCM method to the spiced salt noisy image of 238011 images；
3 (p) be the segmentation result with existing IFCM method to the spiced salt noisy image of 238011 images；
3 (q) be the segmentation result with existing IIFCM method to the spiced salt noisy image of 238011 images；
3 (r) be the segmentation result with the present invention to the spiced salt noisy image of 238011 images；
From figure 3, it can be seen that the present invention can separate background and target on the basis of inhibiting noise, image is poly- Class number can be determined adaptively, insensitive to the initial value of cluster centre, be better than to the segmentation effect of noisy image existing FCM method, FCM_S1 method, FCM_S2 method, FGFCM method, IFCM method and IIFCM method.

Claims (9)

1. the image partition method based on intuitionistic fuzzy C mean cluster, which is characterized in that include the following:
(1) image to be split is inputted, if image to be split is color image, is first converted into gray level image；
(2) maximum number of iterations T=100, outage threshold ε=10 initiation parameter: are set-5, Fuzzy Weighting Exponent m=2, neighborhood window Radius ω=3, primary iteration number t=1, clusters number default initial values Y=2；
(3) using the intuitionistic Fuzzy Sets based on switch mean value constructing tactics to noise robustnessWherein, 1≤i≤n, n indicate picture Plain total number；
(4) it is directed to the grey value profile of input picture, is carried out curve fitting using least square method, selects the peak value of matched curve Cluster centre value of the corresponding gray level of point as initialization, and the range of estimation cluster numbers is set as { 2 ..., Ymax, cluster Center initial value range set C isWherein, cYIndicate the initial value of the Y cluster centre, 1≤Y≤Ymax, YmaxIndicate the maximum value of clusters number, 2≤Ymax≤ n, T indicate transposition；
(5) linear weighting function coefficient H is iterated to calculateir, Intuitionistic Fuzzy DistancesWith intuitionistic fuzzy objective function J:
(5a) constructs linear weighting function coefficient H using the location information and grayscale information of pixelir:
Wherein, (ai,bi) and (ar,br) respectively indicate the spatial value of center pixel i and neighborhood territory pixel r, xiAnd xrTable respectively Show the gray value of center pixel i and neighborhood territory pixel r, ukiIndicate xiFor k-th of cluster centre ckDegree of membership, 1≤k≤Y, 1 ≤r≤ω2；
(5b) calculates pixel x under intuitionistic Fuzzy SetsiTo the Intuitionistic Fuzzy Distances of cluster centre ckWherein,With Respectively indicate pixel xiWith the intuitionistic Fuzzy Sets of cluster centre ck；
The linear weighting function coefficient H that (5c) is obtained in conjunction with (5a)irThe Intuitionistic Fuzzy Distances obtained with (5b)It calculates Intuitionistic fuzzy objective function J, and solve the subordinating degree function u in intuitionistic fuzzy objective function JkiWith cluster centre ckIntuition mould Paste collection
(6) judge iteration stopping condition | | C(t+1)-C(t)| | whether < ε or operation the number of iterations t > T are true:
If invalid, t=t+1 is enabled, is returned (5)；
If set up, exports subordinated-degree matrix U and judge Y < YmaxIt is whether true, if so, Y=Y+1 is then enabled, cluster centre is taken The preceding Y numerical value of initial value range set C, enables the initial value set of cluster centre beIt enables Primary iteration number t=1 is returned (5), if Y < YmaxIt is invalid, it executes (7), whereinFor 0 cluster centre numerical value set,Indicate the Q cluster centre iteration t Secondary intuitionistic Fuzzy Sets,Indicate the initial value of the P cluster centre, 1≤Q≤Y, 1≤P≤Y, U={ uki}；
(7) (Y is inputted from (6)max- 1) a subordinated-degree matrix U, and evaluate these using classification range index evaluation index GD and be subordinate to Spend matrix；If the maximum subordinated-degree matrix of GD numerical value is Umax, by UmaxCorresponding clusters number YfinAs the final of input picture Clusters number, wherein 1≤Yfin≤Ymax；
(8) to the subordinated-degree matrix U of (7) outputmaxPixel classifications are done, and carry out error detection classification, export segmented image:
(8a) is according to maximum membership grade principle and final clusters number Yfin, to the subordinated-degree matrix U of (7) outputmaxPixel into Row classification, output label matrix UL, wherein ULInside contain only categorization values 1,2 ..., YW..., Yfin, 1≤YW≤Yfin；
(8b) sets the label class label of center pixel i as Li, multiply in 5 neighborhoods the 5 of center pixel i, judge label class label Li's Pixel quantity nLWhether n is metL≤25/(Yfin+ 1) condition:
If it is satisfied, then determining that center pixel i divides pixel for mistake, and mistake is divided to the label class label L of pixeliIt is modified in neighborhood The most label class label of label class label quantity of the same race executes (8c)；
If conditions are not met, then determining that center pixel i is correct distribution pixel, execute (8c)；
(8c) judges label matrix ULIn pixel whether all traverse, if so, exporting final label matrix UF, it executes (8d), Continue to traverse if it is not, returning to (8b)；
(8d) is to final label matrix UFMiddle label class label is YWPixel assign different corresponding gray value xW, and will assign The matrix of different gray values is exported as segmented image.
2. the method according to claim 1, wherein being used in (3) based on switch mean value constructing tactics to noise The intuitionistic Fuzzy Sets of robustIt is accomplished by
(3a) first determines whether the grey scale pixel value multiplied in 3 neighborhoods the 3 of ith pixel is not all 0 or 255:
If set up, the pixel that gray value is 0 and 255 is taken out, then take the mean value of rest of pixels gray value, and be set as center The amendment numerical value of pixel executes (3b)；
If invalid, center pixel gray value is not modified, is executed (3b)；
(3b) judges whether the pixel in input picture all traverses: if so, output amendment image, executes (3c), if not It is to return to (3a) to continue to traverse；
The grey scale pixel value for correcting image is calculated as intuitionistic Fuzzy Sets by (3c)
(3c1) sets μ (xi) indicate grey scale pixel value xiDegree of membership, v (xi) and π (xi) it is x respectivelyiIt is constructed using Yager operator Non-affiliated degree and hesitation degree, calculation formula difference it is as follows:
π(xi)=1- μ (xi)-v(xi),
Wherein, 0≤μ (xi)≤1,0≤v (xi)≤1,0≤μ (xi)+v(xi)≤1, α=0.8 indicates Yager operator coefficient；
The result construction intuitionistic Fuzzy Sets that (3c2) is obtained according to (3c1)
3. the method according to claim 1, wherein the grey value profile of input picture is directed in (4), using most Small square law carries out curve fitting, and corresponding gray level is selected to be accomplished by as the cluster centre value of initialization
Gray level image is set as 256 gray levels by (4a), and grey level range is [0,255], and the gray scale for drawing gray level image is straight Fang Tu；
(4b) counts the grey value profile frequency f (l) of the gray level l on grey level histogram, and does seven rank least square method curves Fitting, wherein 0≤l≤255；
(4c) seeks the first derivative f'(l of f (l)), compare f'(l) with f (l) before and after gray level first derivative f'(l-1) and f' (l+1), as l=0 l=255, only judge f'(l) whether less or greater than 0:
As l=0, f'(l is judged) whether < 0 is true, if so, being exported gray level l=0 as peak point；
As 0 < l < 255, f'(l is judged)=0 and f'(l-1) > 0 and f'(l+1) whether < 0 true, if so, by gray scale Grade l is exported as peak point；
As l=255, f'(l is judged) whether > 0 is true, if so, being exported gray level l=255 as peak point；
(4d) judges whether 256 gray levels all traverse:
If so, output peak point, executes (4e), continue to traverse if it is not, returning to (4c)；
(4e) sorts to the peak point that (4d) is exported according to the sequence of intensity profile frequency values from big to small, counts the number of peak point Mesh is Ymax, and the corresponding gray level l of peak point sorted from large to small is counted, obtaining gray scale value range isIt is cluster centre initial value that gray level, which is corresponded to numerical statistic, such asIf cluster centre initial value range set C isWherein, lYIt indicates in peak point intensity profile frequency values according in descending order, the Gray level corresponding to Y peak point intensity profile frequency values.
4. the method according to claim 1, wherein calculating grey scale pixel value x under intuitionistic Fuzzy Sets in (5b)iIt arrives Cluster centre ckIntuitionistic Fuzzy DistancesIt is accomplished by
(5b1) sets μ (ck) indicate cluster centre ckDegree of membership, v (ck) and π (ck) respectively indicate cluster centre ckUse Yager The non-affiliated degree and hesitation degree of operator construction, calculation formula difference are as follows:
π(ck)=1- μ (ck)-v(ck),
Wherein, 0≤μ (ck)≤1,0≤v (ck)≤1,0≤μ (ck)+v(ck)≤1；
(5b2) calculates Intuitionistic Fuzzy Distances according to the result that (5b1) is obtained
Wherein, μ (xi)、v(xi) and π (xi) respectively indicate grey scale pixel value xiDegree of membership, non-affiliated degree and hesitation degree.
5. the method according to claim 1, wherein utilizing the local spatial information of pixel and combination in (5c) The linear weighting function coefficient H that (5a) is obtainedirThe Intuitionistic Fuzzy Distances obtained with (5b)Calculate intuitionistic fuzzy mesh Scalar functions J, formula are as follows:
Wherein, ukiIt is pixel xiTo cluster centre ckSubordinating degree function, m indicate FUZZY WEIGHTED index.
6. according to the method described in claim 5, it is characterized in that, using lagrange's method of multipliers from intuitionistic fuzzy objective function Subordinating degree function u is obtained in JkiWith cluster centre ckIntuitionistic Fuzzy SetsIt is accomplished by
(5c1) introduces Lagrange multiplier λ in intuitionistic fuzzy objective function Ji, construct the target letter with Lagrange multiplier Number L:
(5c2) calculates separately L for subordinating degree function uki, cluster centre ckIntuitionistic Fuzzy SetsWith Lagrange multiplier λi's Partial derivativeWithAnd in partial derivativeUnder conditions of, by acquiring u as followskiWith
Wherein, k=1,2 ..., Y, i=1,2 ..., n, r=1,2 ..., ω2。
7. the method according to claim 1, wherein being classified the formula table of range index evaluation index GD in (7) Up to as follows:
Wherein, ui1It indicates that ith pixel is maximum and is subordinate to angle value, ui2Indicate ith pixel it is second largest be subordinate to angle value.
8. the method according to claim 1, wherein the maximum membership grade principle in (8a), is according to Vk=argk {max(uki) pixel i is divided into the maximum classification V of degree of membershipk。
9. the method according to claim 1, wherein to final label matrix U in (8d)FMiddle label class label is YWPixel assign different corresponding gray value xW, formula is as follows:
xW=[255/YW]
Wherein, [] indicates to take internal integer, final label matrix UFInside contain only categorization values 1,2 ..., YW,…,Yfin}。
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