CN112784877A - Large-scale image template matching method and device based on block chain - Google Patents
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Abstract
The invention belongs to the technical field of block chains, and provides a large-scale image template matching method, a device, a system, computer equipment and a computer readable storage medium based on a block chain, extracting the template features of a plurality of edge device image templates, uploading the template features to a block chain, obtaining the image features of the retrieval image extracted by the edge device, verifying the template features and the image features by the edge device through the block chain, comparing the template features with the image features, feeding back the matched comparison result to the edge device, because only the image characteristics and the template matching result are uploaded and downloaded between the edge device and the server, the information such as the original image and the like is not transmitted, the privacy is ensured, the transmission cost is reduced, meanwhile, different edge devices upload training data, so that the precision of the comparison model can be improved, the template features are protected by using the block chain, and the template features are prevented from being abused by unverified devices.
Description
Technical Field
The present application relates to the field of blockchain technology, and in particular, to a method and an apparatus for matching a large-scale image template based on a blockchain, a computer device, and a computer-readable storage medium.
Background
With the rapid development of the technology industries such as cloud computing, big data, internet of things and the like, the data traffic growth rate is continuously accelerated. For example, it is predicted that the total amount of global data will currently reach 44ZB (1 ZB-10 billion TB-1 trillion GB).
Meanwhile, the popularization of digital photography and the development of storage and propagation technologies also lead the number of images to show large-scale growth. How to identify and manage such a large scale and convert disordered image data into image information useful for human beings is an engineering practice proposition with long-term technical and commercial value.
In the prior art, image classification techniques are widely used for effective image recognition. Image classification techniques are an important component of the field of pattern recognition and machine vision. For untrained machines, the image is just a combined discrete pixel point, but the image classification technology can improve the understanding gap between the machine and the human by extracting and classifying the characteristic information of the image data. In recent years, application fields such as internet image search or video search have been produced using image classification techniques.
In addition, the image recognition technology based on template matching is widely applied due to the simplicity and the easy use of the method. However, template matching requires the use of a large number of templates, especially in the context of edge computation. Meanwhile, the processing of a large amount of image template data inevitably involves consideration of data security, storage transmission, identification precision and the like, so how to effectively manage equipment and templates, ensure template data security, reduce template storage capacity and improve template matching accuracy rate becomes a technical problem to be solved urgently in an image identification technology based on template matching.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a method, an apparatus, a system, a computer device, and a computer-readable storage medium for large-scale image template matching based on a block chain.
The server is used as an execution subject, and the large-scale image template matching method based on the block chain is provided and comprises the following steps:
extracting template features of a plurality of edge device image templates and uploading the template features to a block chain;
acquiring image features of a retrieval image extracted by the edge device, wherein the edge device passes through the block chain verification;
and comparing the template features with the image features, and feeding back a matched comparison result to the edge device.
The block chain is taken as an execution subject, and the large-scale image template matching method based on the block chain is provided and comprises the following steps:
storing template features extracted from a plurality of edge device image templates;
verifying the identity of the edge device initiating the image matching request so as to authorize a server to obtain image features of a retrieval image extracted by the edge device;
and transmitting the template features to the server so that the server can perform feature comparison on the template features and the image features, and feeding back a matched comparison result to the edge device.
A large-scale image template matching system based on a block chain comprises a plurality of edge devices, a server and the block chain;
the server extracts template features of the edge device image templates and uploads the template features to the block chain;
the server acquires image features of a retrieval image extracted by the edge device, and the edge device is verified through the block chain;
and the server compares the template features with the image features and feeds back a matched comparison result to the edge equipment.
A large-scale image template matching device based on a block chain, which runs in a server and comprises:
the extraction uploading module is used for extracting template features of a plurality of edge equipment image templates and uploading the template features to the block chain;
the retrieval acquisition module is used for acquiring the image characteristics of the retrieval image extracted by the edge device, and the edge device passes the block chain verification;
and the comparison feedback module is used for comparing the template characteristics with the image characteristics and feeding back the matched comparison result to the edge equipment.
A large-scale image template matching device based on a block chain, which operates in a block chain system, comprises:
the storage module is used for storing template features extracted from a plurality of edge device image templates;
the verification authorization module is used for verifying the identity of the edge device which initiates the image matching request so as to authorize a server to obtain the image characteristics of the retrieval image extracted by the edge device;
and the transmission module is used for transmitting the template features to the server so that the server can perform feature comparison between the template features and the image features and feed back a matched comparison result to the edge device.
A computer device comprising a memory and a processor, the memory storing a computer program, the computer program executing in the processor any of the methods described above.
A computer-readable storage medium storing a computer program which, when executed on a processor, implements any of the methods described above.
The invention provides a large-scale image template matching method, a large-scale image template matching device, a large-scale image template matching system, a large-scale image template matching computer device and a computer readable storage medium based on block chains.
Drawings
FIG. 1 is a block chain-based framework diagram of a large-scale image template matching system according to an embodiment;
fig. 2 is a schematic flowchart of a block chain-based large-scale image template matching method according to an embodiment;
FIG. 3 is a diagram illustrating template matching according to an embodiment;
FIG. 4 is a block chain-based large-scale image template matching apparatus according to an embodiment;
fig. 5 is a schematic block diagram of a computer device according to an embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for matching a large-scale image template based on a blockchain provided by this embodiment can be applied to the application environment shown in fig. 1, in which an edge device communicates with a server through a network, and the server operates in a blockchain system. Among other things, the edge device may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
It should be understood that the apparatus 1, the apparatus 2 and the apparatus N in fig. 1 are schematic diagrams of the edge apparatus of the present invention.
In one embodiment, as shown in fig. 2, a large-scale image template matching method based on block chains is provided, which runs in a server and includes the following steps:
s1, extracting template features of a plurality of edge device image templates and uploading the template features to a block chain;
s2, acquiring image features of the retrieval image extracted by the edge device, and verifying the edge device through a block chain;
and S3, performing feature comparison on the template features and the image features, and feeding back a matched comparison result to the edge device.
In this embodiment, only the image feature and the template matching result are uploaded and downloaded between the edge device and the server, and information such as an original image is not transmitted, so that the technical effects of guaranteeing privacy and reducing transmission cost can be achieved.
It should be noted that, the image features are corresponding extraction of original image data, and the data volume occupies a small space, so that the transmission cost can be effectively reduced in transmission. Especially in an environment with weak data transmission capability of the block chain, the reduction of the transmission cost by the scheme can also reduce the load of the whole block chain system and improve the running speed of the block chain system.
It should be further noted that the block chain-based large-scale image template matching method operating in the block chain system and the block chain-based large-scale image template matching method operating in the server are corresponding schemes, and for avoiding redundancy, the technical principle and effect are not separately described.
In step S1, template features are uploaded to the block chain, so that data privacy can be enhanced, and security management can be performed on the template data.
It should be noted that the server acquires template features from the plurality of edge devices to form a template feature library, and uploads the template feature library to the block chain, so that a large amount of data training can be provided for the image comparison model, and the comparison model is optimized. Wherein, the template characteristic chain can comprise the following steps:
first, upload the device serial number, template features and corresponding object categories (e.g., people, animals, etc.) to a server;
secondly, respectively generating an MD5 code by the equipment serial number, the template characteristics and the corresponding object type;
third, after verification of the PBFT consensus mechanism, uplink is performed.
It should be further noted that the template features are from image acquisition and feature extraction of the edge device, and specifically may include the following steps:
first, image acquisition. Any camera can be used for image shooting, and the existing picture can be directly input.
Second, target selection is identified. The target to be recognized may be manually framed in the global image, or an image containing only the target may be directly input.
Thirdly, extracting the template features, wherein the step can specifically comprise the following steps:
A. scaling the picture to 49 × 49 size;
B. the picture was evenly divided into 7 x 7 small blocks, for a total of 49 small blocks
C. In each small block, taking a pixel at the center of the small block as a center, and calculating a gradient value;
D. the direction with the maximum gradient value, namely the main gradient direction, is reserved;
E. quantizing the gradient direction into 8 angles, namely 0 degree, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees and 315 degrees;
F. the above template features are represented by a three-digit binary number, as listed below:
| main gradient direction | Characteristic value |
| 0° | 000 |
| 45° | 001 |
| 90° | 010 |
| 135° | 011 |
| 180° | 100 |
| 225° | 101 |
| 270° | 110 |
| 315° | 111 |
G. And (5) arranging the feature values of the 49 small blocks from left to right and from top to bottom sequentially to obtain a template feature body of the picture.
It should be noted that, in the present embodiment, the terms "A, B, C, D" and the like are used only for the purpose of segmentation to achieve clarity and conciseness, and are not used to limit the sequence of specific steps.
In step S2, the edge device is going to retrieve an image, and by providing the image characteristics of the image to the server, the server can authenticate the device through the blockchain, ensuring that the template characteristics on the blockchain are not abused by the unverified device.
Step S3 may further include the steps of:
comparing the data block of the template characteristic with the data block of the image characteristic;
and feeding back the comparison result of which the matching rate is the dominance score to the edge device.
The implementation can refer to fig. 3 and the following scheme:
first, the search image is divided into 7 × 7 patches, and specifically, the main gradient direction feature of each patch is calculated in the same manner as in step C, D, E, F;
secondly, the equipment uploads an equipment serial number to the server and carries out verification in a alliance chain;
thirdly, the main gradient direction characteristics of the image needing template matching can be uploaded through verified equipment;
fourth, the stored template is fetched and matched with the blocks in the image, as shown in fig. 3 (two small blocks constituting the target, actually 7 × 7 to 49 small blocks, are exemplified in fig. 3), from left to right, from top to bottom, using a sliding window method.
For example, two sliding windows marked by a rectangular frame are used, small blocks are taken out from the windows to form features, the features and the template features of the target are subjected to 'bitwise exclusive or' calculation, if the result is not 0, the two blocks are matched, and the matching result of 49 small blocks is obtained in total, namely the local score;
if the sum of the local scores is greater than 25, it indicates that the target is present in the region;
and fifthly, sending back the template matching result to the equipment. Wherein the matching result comprises the category and the position of the target.
In the above example, if the actual size of the data block used for matching is 7 × 7 to 49 data blocks, the local score statistics is performed after the calculation of "bitwise exclusive or" is performed on the 49 data blocks one by one, and if the sum of the local scores is greater than 25, it indicates that the target exists in the region.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, referring to fig. 4, a block chain-based large-scale image template matching apparatus is provided, which corresponds to the block chain-based large-scale image template matching method in the above embodiment one to one. As shown in fig. 4, the apparatus includes:
the extraction uploading module 1 is used for extracting template features of a plurality of edge device image templates and uploading the template features to a block chain;
the retrieval acquisition module 2 is used for acquiring the image characteristics of the retrieval image extracted by the edge device, and the edge device is verified through a block chain;
and the comparison feedback module 3 is used for comparing the template characteristics with the image characteristics and feeding back the matched comparison result to the edge device.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such process, method, article, or apparatus, and that the division of modules presented in this application is merely a logical division and may be implemented in a practical application in another manner.
It should be further noted that, for specific definitions and descriptions of the above apparatus, reference may be made to the above definitions and descriptions of the block chain-based large-scale image template matching method, and details are not repeated here. The various modules in the above-described apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, referring to fig. 4 and 5, a computer device is provided, which may be a server running the extraction uploading module 1, the retrieval obtaining module 2 and the comparison feedback module 3. The internal structure of the computer device may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data involved in a large-scale image template matching method based on block chains. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a blockchain based large scale image template matching method.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps of the block chain-based large-scale image template matching method in the above embodiments are implemented, for example, steps 1 to 3 shown in fig. 2 and other extensions of the method and extensions of related steps. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units of the block chain-based large-scale image template matching apparatus in the above-described embodiment, such as the functions of the modules 1 to 3 shown in fig. 4. To avoid repetition, further description is omitted here.
The ProceSsor may be a Central ProceSsing Unit (CPU), other general purpose ProceSsor, a Digital Signal ProceSsor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the computer device and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the computer device by executing or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc.
The memory may be integrated in the processor or may be provided separately from the processor.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the blockchain-based large-scale image template matching method in the above-described embodiments, such as the steps 1 to 3 shown in fig. 2 and extensions of other extensions and related steps of the method. Alternatively, the computer program is executed by a processor to implement the functions of the modules/units of the block chain-based large-scale image template matching apparatus in the above-described embodiment, for example, the functions of the modules 1 to 3 shown in fig. 4. To avoid repetition, further description is omitted here.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A large-scale image template matching method based on a block chain is characterized by comprising the following steps:
extracting template features of a plurality of edge device image templates and uploading the template features to a block chain;
acquiring image features of a retrieval image extracted by the edge device, wherein the edge device passes through the block chain verification;
and comparing the template features with the image features, and feeding back a matched comparison result to the edge device.
2. The method of claim 1, wherein the comparing the template features with the image features and feeding back the matching comparison results to the edge device comprises:
comparing the data block of the template characteristic with the data block of the image characteristic;
and feeding back a comparison result of which the matching rate is the dominance score to the edge device.
3. A large-scale image template matching method based on a block chain is characterized by comprising the following steps:
storing template features extracted from a plurality of edge device image templates;
verifying the identity of the edge device initiating the image matching request so as to authorize a server to obtain image features of a retrieval image extracted by the edge device;
and transmitting the template features to the server so that the server can perform feature comparison on the template features and the image features, and feeding back a matched comparison result to the edge device.
4. The method of claim 3, comprising: transmitting the template features to the server such that the server can perform the steps of:
comparing the data block of the template characteristic with the data block of the image characteristic;
and feeding back a comparison result of which the matching rate is the dominance score to the edge device.
5. A large-scale image template matching system based on a block chain comprises a plurality of edge devices, a server and the block chain;
the server extracts template features of the edge device image templates and uploads the template features to the block chain;
the server acquires image features of a retrieval image extracted by the edge device, and the edge device is verified through the block chain;
and the server compares the template features with the image features and feeds back a matched comparison result to the edge equipment.
6. A large-scale image template matching apparatus based on block chains, comprising:
the extraction uploading module is used for extracting template features of a plurality of edge equipment image templates and uploading the template features to the block chain;
the retrieval acquisition module is used for acquiring the image characteristics of the retrieval image extracted by the edge device, and the edge device passes the block chain verification;
and the comparison feedback module is used for comparing the template characteristics with the image characteristics and feeding back the matched comparison result to the edge equipment.
7. The apparatus of claim 6, wherein the alignment feedback module comprises:
the comparison unit is used for comparing the data block of the template characteristic with the data block of the image characteristic;
and the feedback unit is used for feeding back the comparison result of which the matching rate is the dominance score to the edge equipment.
8. A large-scale image template matching apparatus based on block chains, comprising:
the storage module is used for storing template features extracted from a plurality of edge device image templates;
the verification authorization module is used for verifying the identity of the edge device which initiates the image matching request so as to authorize a server to obtain the image characteristics of the retrieval image extracted by the edge device;
and the transmission module is used for transmitting the template features to the server so that the server can perform feature comparison between the template features and the image features and feed back a matched comparison result to the edge device.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the computer program is operative to perform the method of any of claims 1-4 in the processor.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program is executed in a processor to implement the method of any of claims 1-4.
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