CN111178409B - Image matching and recognition system based on big data matrix stability analysis - Google Patents
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Abstract
本发明提出一种基于大数据矩阵稳定性分析的图像匹配与识别系统、方法与计算机可读存储介质。本发明的技术方案对于输入的原始图像，不用进行预处理，而是在查询之前首先进行识别处理，从原始图像中识别出一个或者一组包含原始图像的最多关键要素的子图像，再基于子图像进行图像匹配搜索。识别出包含最多关键要素的方法包括相似度判断以及特征矩阵稳定性判断，从而使得输入的查询检索要素既能够包含原始输入图像的关键信息，又能够极大的降低数据处理量，从而保证了在不丢失图像主要信息的前提下来提高图像检索速度。本发明的上述方案能够通过自动化的计算机图像识别程序与匹配程序进行，无需人工干预与先验经验。
The invention provides an image matching and recognition system, method and computer readable storage medium based on the stability analysis of big data matrix. The technical solution of the present invention does not need to perform preprocessing on the input original image, but firstly performs identification processing before querying, and identifies one or a group of sub-images containing the most key elements of the original image from the original image. Image for image matching search. The methods for identifying the most critical elements include similarity judgment and feature matrix stability judgment, so that the input query and retrieval elements can not only contain the key information of the original input image, but also greatly reduce the data processing volume, thus ensuring the The image retrieval speed is improved without losing the main information of the image. The above solution of the present invention can be carried out by an automatic computer image recognition program and a matching program without manual intervention and prior experience.
Description
Technical Field
The invention belongs to the technical field of image recognition, and particularly relates to an image matching and recognition system and method based on big data matrix stability analysis and a computer readable storage medium.
Background
The development of mobile internet, smart phones and social networks brings huge amounts of picture information, and the uploading amount of images of an Instagram per day is about 6000 million; the daily picture delivery amount of WhatsApp is 5 hundred million; the WeChat friend circle in China is also driven by picture sharing. Pictures which are not limited by regions and languages gradually replace complicated and delicate characters and become main media for transferring word meaning. The reason why the pictures become the main media for internet information exchange is mainly two reasons: firstly, from the habit of reading information by a user, compared with characters, the picture can provide more vivid, easily understood, interesting and artistic information for the user; secondly, from the view of picture sources, the smart phone brings convenient shooting and screen capturing means for people, and helps people to acquire and record information by pictures more quickly.
But with pictures becoming the main information carrier in the internet, difficulties arise. When the information is recorded by characters, the required content can be easily found through keyword search and can be edited at will, and when the information is recorded by pictures, the content in the pictures cannot be retrieved, so that the efficiency of finding the key content from the pictures is influenced. The picture brings a fast information recording and sharing mode to us, but reduces the information retrieval efficiency. Under such circumstances, the image recognition technology of the computer is very important.
Image recognition is a technique in which a computer processes, analyzes, and understands images to recognize various different patterns of objects and objects. The identification process comprises image preprocessing, image segmentation, feature extraction and judgment matching. In brief, image recognition is how a computer reads the contents of a picture like a person. By means of the image recognition technology, the information can be acquired more quickly through picture searching, a new mode of interacting with the external world can be generated, and the external world can operate more intelligently.
Picture search is different from text search. The prior art picture search function is generally to provide a source image, pre-process it, and input it into a search engine (e.g., a picture matching system) to obtain at least one target image meeting specific requirements as an output. In the process, complicated preprocessing needs to be carried out on the source image, a large amount of characteristic information is extracted, and then semantic-based retrieval is carried out according to the processed image characteristic information.
The retrieval system disclosed by the Chinese patent with the publication number of CN103927387B and the related method and device thereof extract the characteristics of a sample image or a preprocessed sample image, and the extracted characteristic data comprises the position information, the scale, the direction and the characteristic description information of each characteristic point in an image area; classifying the feature description information of the sample image by using a classifier to find out an optimal classification result, wherein each classification corresponds to a classification index after classification; performing dimension reduction processing on the feature description information by combining the classification index value of the classification to which each feature description information belongs, taking the result data after dimension reduction as a label corresponding to the feature point, wherein each feature point corresponds to one label data; sequentially storing sample image content data into a retrieval database in a unit of sample image index, the content data of one sample image comprising: sample image index values, the number of characteristic points, and position information, scale, direction and characteristic description information of each characteristic point; and sequentially storing the label data into a retrieval database according to the classification index values in each classification by taking the label as a unit, wherein each label data corresponds to one classification index value, and each classification index value corresponds to a group of similar label data sets. Before the classifying is carried out on the feature description information of the sample image by using the classifier, the method further comprises the following steps: judging whether a classifier exists; if yes, classifying the feature description information of the sample image according to the existing classifier; if not, training a data set formed by the feature description information of all the sample images to generate a classifier. Preferably, training a data set composed of the feature description information to generate a classifier specifically includes: and generating a plurality of clustering centers by adopting a K-means clustering algorithm, and classifying the description data according to the distribution condition of the clustering centers by using a neighbor method. Preferably, the dimensionality reduction of the feature description information is to generate a dimensionality reduction matrix by adopting a Principal Component Analysis (PCA) method.
The chinese patent application with the application number of cn201811608994.x provides an image retrieval method and a related product, wherein an image retrieval device can compare each face image in a first face image set with each face image in a second face image set to obtain a plurality of comparison values, the comparison values can be understood as the similarity between each face image in the first face image and each face image in the second face image, if the comparison values are larger, the greater the similarity is, the greater the probability of representing the same face image is, the maximum comparison value can be selected from the plurality of comparison values, and the face image corresponding to the maximum comparison value is taken as a target object, so that the target object can be confirmed, and the accuracy of identifying the target object is improved;
chinese patent application No. CN201810903853.4 proposes an image retrieval method and system based on image segmentation and fuzzy pattern recognition, which performs image segmentation, sequentially performs similarity matching on each image region at the same position corresponding to a query image and each retrieved image, and comprehensively considers the similarity between each image region to measure the similarity between the query image and each retrieved image, thereby further enhancing the contrast between the images; in the image feature extraction process, the color feature and the texture feature of the image are comprehensively considered, so that the representativeness of the image feature is better than that when the color feature or the texture feature is considered independently; in the process of extracting the color features and the texture features of the image, a fuzzy mathematical algorithm is introduced, so that the representativeness of the color features and the texture features to the image can be further improved; the similarity between the query image and each image to be searched is comprehensively measured by utilizing the similarity between the k adjacent images of the query image and the k adjacent images of each image to be searched, so that the performance of the image retrieval system can be further improved; the neighbor number k is set as a dynamic parameter through a function, so that the adaptability of the image retrieval system to different query images can be further improved; and the performance of the image retrieval system can be further improved by carrying out information feedback through the satisfaction degree of the user.
The Chinese invention patent application with the application number of CN201810296979.X provides an image retrieval method and an image storage method, wherein a node of a service searching cluster extracts characteristics of an input image, the image and the characteristic data of the image are stored separately, an original image is stored in a database, and the characteristic data is stored in each node, so that each node can be matched with the locally stored image characteristic data independently during image retrieval.
However, in the above image identification or retrieval scheme in the prior art, the image to be queried or retrieved needs to be preprocessed mostly, including links such as image automation processing and manual labeling, and for each image, as many identification features as possible need to be extracted, and for a single image, the calculation amount is still barely acceptable; however, if the search engine is applied to a large amount of large-scale images, the above process will bring a large amount of computational complexity at the same time, so that the search efficiency is reduced.
Disclosure of Invention
In order to solve the technical problems, the invention provides an image matching and recognition system and method based on big data matrix stability analysis and a computer readable storage medium. According to the technical scheme, input original images are not preprocessed, but are firstly identified before being inquired, one or a group of sub-images containing the most key elements of the original images are identified from the original images, and then image matching search is carried out on the basis of the sub-images. The method for identifying the key elements with the most number comprises similarity judgment and characteristic matrix stability judgment, so that the input query retrieval elements can contain the key information of the original input image, the data processing amount can be greatly reduced, and the image retrieval speed is improved on the premise of not losing the main information of the image. The scheme of the invention can be carried out through an automatic computer image recognition program and a matching program without manual intervention and prior experience.
In particular, in the first aspect of the invention, a large data matrix stability analysis-based identification system is provided, which comprises an image segmentation module, an image feature extraction module, a feature data matrixing engine, a feature data stability judgment component and an identification feature output interface,
the image segmentation component is used for picking up at least one sub-image of an initial input image, and the size of the sub-image is smaller than that of the initial input image;
the feature extraction module is used for extracting a plurality of sub-image feature points aiming at the sub-images;
the characteristic data matrixing engine is used for forming a sub-image characteristic data matrix based on the extracted plurality of sub-image characteristic points;
the characteristic data stability judging component is used for judging the stability of a characteristic data matrix and outputting the stability on the identification characteristic output interface;
as one of the key technologies for embodying the innovation point of the present invention, the characteristic data stability determining component is configured to determine the stability of the characteristic data matrix, and specifically includes:
based on the plurality of sub-image feature points extracted aiming at the sub-images, restoring at least one comparison image by adopting an image reconstruction technology;
determining a similarity of the comparison image and the initial input image,
if the similarity is smaller than a preset threshold value, extracting a plurality of initial image feature points from an initial input image by using the feature extraction module;
the characteristic data matrixing engine is used for forming an initial image characteristic data matrix based on the extracted plurality of initial image characteristic points;
obtaining a combination matrix of the sub-image characteristic data matrix and the initial image characteristic data matrix,
if the trace of the combined matrix is larger than a preset value, outputting an identification result on the identification feature output interface;
if the similarity is greater than a predetermined threshold, updating the initial input image to at least one currently picked-up sub-image; the sub-image characteristic data matrix and the initial image characteristic data matrix are N-order matrixes with equal size, and N is a positive integer larger than 1.
It should be noted that, in the above recognition process, the size ratio of the sub-image to the initial input image is greater than a preset ratio value.
Preferably, the difference operation may be performed on the sub-image feature data matrix and the initial image feature data matrix to obtain the combination matrix.
Additionally, the initial input image is updated to the currently picked up at least one sub-image if the trace of the combined matrix is smaller than a predetermined value.
In a second aspect of the present invention, there is provided an image recognition method, which adopts the above-mentioned recognition system based on big data matrix stability analysis to perform a pattern recognition process and output at least one initial input image as a source input image (a retrieval image to be queried) of a subsequent image matching or retrieval system, the method comprising the following steps:
step S501: acquiring an original input image, and taking the original input image as an initial input image;
step S502: picking up at least one sub-image from the initial input image based on the set preset proportion value;
step S503: extracting a plurality of sub-image feature points of the sub-images;
step S504: reconstructing at least one comparison image based on the extracted plurality of image feature points;
step S505: judging the similarity between the comparison image and the initial input image;
step S506: judging whether the similarity is larger than a preset threshold value or not;
if yes, taking at least one currently picked sub-image as an initial input image, and returning to the step S502;
if not, go to step S507;
step S507: extracting a plurality of initial input image feature points of the initial input image;
step S508: respectively obtaining a sub-image characteristic data matrix and an initial image characteristic data matrix based on the image characteristic points extracted in the steps S503 and S507;
step S509: obtaining a combined matrix based on the sub-image characteristic data matrix and the initial image characteristic data matrix;
step S510: determining whether a trace of the combined matrix is less than a predetermined value,
if yes, taking at least one currently picked sub-image as an initial input image, and returning to the step S502;
otherwise, outputting the initial input image on the identification feature output interface.
In step S509, the method performs a difference operation on the sub-image characteristic data matrix and the initial image characteristic data matrix in combination with the preset ratio value to obtain the combined matrix.
In a third aspect of the present invention, an image matching system is provided, the image matching system is used for retrieving a matching image, for example, inputting an image to be inquired, and the image matching system outputs at least one target image meeting a predetermined condition.
Different from the prior art, the image matching system provided by the invention processes the image to be inquired through the image recognition system provided by the invention after the image to be inquired is input, so as to obtain at least one initial input image, and then inquires.
Preferably, if a plurality of initial input images are output, an initial input image having the greatest similarity to the original input image is taken as the input query image.
In a fourth aspect of the present invention, an image matching method is further provided, where the method queries, for a source input image, at least one target image whose matching degree meets a predetermined requirement in a massive image library, and specifically includes: and identifying the source input image by using the image identification method, outputting at least one initial input image, taking the initial input image as an input query image, and querying at least one target image with matching degree meeting a preset requirement in a massive image library.
The above methods of the present invention can be implemented automatically in the form of a computer program of instructions, and the execution process does not require human intervention, therefore, the present invention further provides a computer-readable storage medium having a computer-executable program of instructions stored thereon, which is executed by a processor and a memory, for implementing the aforementioned image recognition method and image matching method.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is an overall architecture diagram of an image recognition system according to an embodiment of the present invention
FIG. 2 is a flow chart of an image recognition method according to an embodiment of the invention
FIG. 3 is a flow chart of an image recognition method according to another embodiment of the present invention
FIG. 4 is an overall architecture diagram of an image matching system of one embodiment of the present invention
FIG. 5 is a flow chart of an image matching method according to an embodiment of the invention
FIG. 6 is a graph comparing image matching accuracy of the present invention with that of the prior art
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 invention is further described with reference to the following drawings and detailed description:
fig. 1 is a diagram illustrating an overall architecture of an image recognition system according to an embodiment of the present invention.
In fig. 1, an identification system based on big data matrix stability analysis is provided, where the identification system includes an image segmentation module, an image feature extraction module, a feature data matrixing engine, a feature data stability determination component, and an identification feature output interface.
In this embodiment, the image segmentation component is configured to pick up at least one sub-image of the initial input image, the sub-image having a smaller size than the initial input image.
Preferably, the size ratio of the sub-image to the initial input image is greater than a preset ratio value.
The preset proportional value may be set in the system in advance, for example, to 90% or 0.95. It should be noted that, in order to enable the technical solution of the present invention to obtain a better recognition effect and enable the recognition result to be used for subsequent matching, the preset ratio value should be relatively high, for example, 90% or more.
As an example, assuming the initial input image size is X X Y, the sub-image size may be a b, where 1 > a/X > 0.9, 1 > b/Y > 0.9
In fig. 1, the feature extraction module is configured to extract a plurality of sub-image feature points for the sub-image.
There are many common image feature extraction methods in the field for extracting a plurality of image feature points for an image, and the present invention does not limit this, and as an illustrative example, a feature value, such as an RGB three-channel value, or an average value thereof, may be extracted for each pixel point of an image; edge recognition can be carried out on the image, and the characteristic value of each edge sub-picture is extracted; or, the picture is transformed, for example, a characteristic value is calculated for each p pixel points;
on the basis, the characteristic data matrixing engine is used for forming a sub-image characteristic data matrix based on the extracted plurality of sub-image characteristic points;
referring to the foregoing example, the feature data matrix may be a Z-order matrix, Z being less than X and less than Y;
as a key technical means for embodying the innovation point of the invention, the recognition system carries out image recognition in the following way:
the characteristic data stability judging component is used for judging the stability of a characteristic data matrix and outputting the stability on the identification characteristic output interface;
specifically, the feature data stability determining component is configured to determine the stability of the feature data matrix, and includes the following steps:
based on the plurality of sub-image feature points extracted aiming at the sub-images, restoring at least one comparison image by adopting an image reconstruction technology;
determining a similarity of the comparison image and the initial input image,
if the similarity is smaller than a preset threshold value, extracting a plurality of initial image feature points from an initial input image by using the feature extraction module;
the characteristic data matrixing engine is used for forming an initial image characteristic data matrix based on the extracted plurality of initial image characteristic points;
obtaining a combination matrix of the sub-image characteristic data matrix and the initial image characteristic data matrix,
if the trace of the combined matrix is larger than a preset value, outputting an identification result on the identification feature output interface;
updating the initial input image to at least one currently picked up sub-image if the trace of the combined matrix is less than a predetermined value;
if the similarity is greater than a predetermined threshold, updating the initial input image to at least one currently picked-up sub-image;
the subimage characteristic data matrix and the initial image characteristic data matrix are N-order matrixes with equal size, N is a positive integer larger than 1, and N is less than or equal to Z;
in this example, the sub-image feature data matrix is Pnext, and the initial image feature data matrix is Pcurrent;
the combined matrix may be Pcurrent-Pcurrent, i.e., a difference matrix of the two.
The image recognition method performed by the above recognition system of the present embodiment may further refer to the flow described in fig. 2-3.
In fig. 2, the method comprises an iterative loop consisting of steps S501-S510, in particular:
step S501: acquiring an original input image, and taking the original input image as an initial input image;
step S502: picking up at least one sub-image from the initial input image based on the set preset proportion value;
step S503: extracting a plurality of sub-image feature points of the sub-images;
step S504: reconstructing at least one comparison image based on the extracted plurality of image feature points;
step S505: judging the similarity between the comparison image and the initial input image;
step S506: judging whether the similarity is larger than a preset threshold value or not;
if yes, taking at least one currently picked sub-image as an initial input image, and returning to the step S502;
if not, go to step S507;
as one of the core concepts of the present invention, if the similarity of the sub-image and the initial input image is greater than the threshold, it means that the currently picked up sub-image can still be reduced again; therefore, the initial input image is updated, and the above process is continued until the picked-up sub-image can not be reduced;
step S507: extracting a plurality of initial input image feature points of the initial input image;
step S508: respectively obtaining a sub-image characteristic data matrix and an initial image characteristic data matrix based on the image characteristic points extracted in the steps S503 and S507;
step S509: obtaining a combined matrix based on the sub-image characteristic data matrix and the initial image characteristic data matrix;
step S510: determining whether a trace of the combined matrix is less than a predetermined value,
if yes, taking at least one currently picked sub-image as an initial input image, and returning to the step S502;
otherwise, outputting the initial input image on the identification feature output interface.
Since the combination matrix is the difference between the sub-image characteristic data matrix and the initial image characteristic data matrix, if the two retained information are similar, the trace of the combination matrix will be smaller than the predetermined value, which means that the sub-image can be further reduced.
As a further preferred option, the difference operation is performed on the sub-image characteristic data matrix and the initial image characteristic data matrix in combination with the preset ratio value to obtain the combined matrix.
For example, the sub-image feature data matrix is Pnext, the initial image feature data matrix is Pcurrent, the preset ratio value is 0.95,
the combined matrix may be Pnext-0.95 Pcurrent.
Through the above process, a sub-image that retains key information of an original image but has a minimum size may be output, and in an implementation, if a plurality of initial input images are output, the initial input image having a maximum similarity to the original input image is taken as an input query image.
The steps included in the method of fig. 2 may be performed in selected portions to provide greater efficiency. The key technical means is to use the trace of the combined matrix to carry out the cyclic judgment. Therefore, referring to fig. 3, a flowchart of an image recognition method according to another embodiment is also provided, and the method of fig. 3 includes the following steps:
picking up at least one sub-image from an initial input image according to the initial input image, extracting a plurality of sub-image characteristic points of the sub-image, and obtaining a sub-image characteristic data matrix
Extracting a plurality of initial input image characteristic points of an initial input image aiming at the initial input image to obtain an initial image characteristic data matrix;
the two steps can be executed in parallel without sequence, thereby improving the calculation efficiency;
on the basis, obtaining a combined matrix, then judging whether the trace of the combined matrix is smaller than a preset value, if so, taking at least one currently picked sub-image as an initial input image, and returning to the processing step aiming at the initial input image;
otherwise, outputting the initial input image on the identification feature output interface.
Fig. 4 is an overall architecture diagram of an image matching system of one embodiment of the present invention.
The image matching is used for retrieving a matching image, for example, an image to be inquired is input, and the image matching system outputs at least one target image meeting a preset condition.
Different from the prior art, the image matching system provided by the invention processes the image to be inquired through the image recognition system provided by the invention after the image to be inquired is input, so as to obtain at least one initial input image, and then inquires.
The image recognition and matching process described in fig. 1-4 does not require preprocessing operations such as noise reduction and smoothing, which are similar to those in the prior art, on the picture itself, nor labeling, including manual labeling or automatic labeling.
Preferably, if a plurality of initial input images are output, an initial input image having the greatest similarity to the original input image is taken as the input query image.
Referring next to fig. 5, fig. 5 is a flowchart of an image matching method according to an embodiment of the invention.
The method shown in fig. 5 is a method for searching at least one target image, of which the matching degree meets a predetermined requirement, in a massive image library for one source input image, and specifically includes: and identifying the source input image by using the image identification method, outputting at least one initial input image, taking the initial input image as an input query image, and querying at least one target image with matching degree meeting a preset requirement in a massive image library.
Fig. 6 is a graph comparing the image matching accuracy of the present invention with the prior art.
In fig. 6, a schematic illustration of the difference between the processing time and the accuracy used in the image recognition and matching process of different orders of magnitude in the solution of the present invention and two common prior art techniques is compared.
One type of prior art is a solution that includes a pre-processing process and another type of prior art is a solution that includes a manual annotation process.
It can be seen that when the number of pictures is extremely small, the recognition accuracy rate of the manual labeling method is the highest, and the processing time can be accepted; however, as the number of pictures increases significantly, manual labeling is hardly possible (e.g., the processing time is not shown to 10 in fig. 6)5Thereafter), and the accuracy decreases drastically; although the scheme including the preprocessing process can be automatically realized, the processing time of the scheme also increases exponentially with the increase of the picture order, the accuracy also decreases, and the scheme cannot be applied to image recognition and matching at a big data level;
in contrast, the scheme of the invention has longer processing time and lower accuracy when the number of pictures is less, but has better effects on both the processing time and the accuracy along with the increase of the order of magnitude of the pictures; according to the matrix stability criterion based on the big data picture characteristics, the larger the number is according to the matrix properties, the higher the stability accuracy embodied by matrix traces is, so that the accuracy is gradually and stably guaranteed along with the increase of the number of pictures; in terms of processing time, the technical scheme of the invention has no complex preprocessing process and labeling process and only performs feature extraction and matrix trace calculation, so that the calculated amount is also obviously reduced, and the processing time tends to be stable when the number of pictures is sharply increased.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
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