CN112528905A - Image processing method and device and computer storage medium - Google Patents
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Abstract
The invention discloses an image processing method, an image processing device and a computer storage medium, wherein the method comprises the following steps: preprocessing an image to be queried; performing visual feature extraction on the preprocessed image to be inquired to obtain a visual feature extraction result; obtaining a visual feature vector based on the visual feature extraction result; extracting text information of the preprocessed image to be inquired to obtain a text characteristic vector; fusing the visual feature vector and the text feature vector to obtain a fused query image vector; and performing similarity calculation between the fused query image vector and the image vector in the database to obtain a picture similar to the image to be queried. By applying the embodiment of the invention, the LBP of a single pixel channel is calculated, the LBP is integrated into the maximum multi-channel LBP to obtain the inter-channel texture information of the color image, the number of the channels of the color image is not depended on, the problem of dimensionality disaster is solved, and the text information and the visual characteristics of the image are fused to improve the retrieval accuracy.
Description
Technical Field
The present invention relates to the field of image retrieval technologies, and in particular, to an image processing method and apparatus, and a computer storage medium.
Background
The content information of the image mainly comprises text semantic information and visual content information, and the existing image technology is divided into two types according to the difference of retrieval objects: one is Text-Based Image Retrieval (TBIR), and the other is Content-Based Image Retrieval (CBIR) Based on the Content of the Image itself. The TBIR constructs an index by using the image file name and surrounding texts, so that the retrieval of the image is converted into the retrieval of text information, and the required image can be returned by matching the keyword input by a user with the image library index. However, the acquisition of image text information mostly depends on manual labeling, and with the rapid increase of the number of images, the TBIR is faced with the problems of time and labor consumption of manual labeling, subjective difference, uncertainty and the like. To overcome the problems faced by TBIR, CBIR is time-consuming. The CBIR establishes the feature vector by using the visual features (color, texture and shape features) of the image, avoids artificial subjectivity, greatly improves accuracy, and improves image retrieval precision by using a similarity measurement algorithm. However, the visual features of the image cannot fully represent the information of the image, and further cannot represent the understanding and perception of people on the image. When observing an image, the user can not only see the visual information such as the color and the texture of the image, but also understand the image by using the visual learning ability of the user and perceive the semantic and the emotion expressed by the image. Which is not expressible by the low-level visual features. This leads to an unavoidable bottleneck problem, namely the "semantic gap" between the low-level visual features and the high-level semantics.
For the retrieval of the signboard image of the merchant, a user uses a mobile phone to shoot the signboard image through specific software so as to retrieve the corresponding merchant and obtain the online information and service of the merchant. Most of signboard images are single in background and possibly repeated in order to highlight store names, and the images are shot by a user through a mobile phone, so that the problems of angle deflection, uneven light, high ambiguity and the like easily occur to the images, and certain difficulty is brought to image retrieval.
Disclosure of Invention
The invention aims to provide an image processing method, an image processing device and a computer storage medium, which aim to overcome the defects in the prior art and achieve better retrieval effect through the fusion of image visual characteristics and text information.
In order to achieve the above object, the present invention provides an image processing method comprising:
preprocessing an image to be queried;
performing visual feature extraction on the preprocessed image to be inquired to obtain a visual feature extraction result, wherein the visual feature extraction result comprises: color features and texture features;
obtaining a visual feature vector based on the visual feature extraction result;
extracting text information of the preprocessed image to be inquired to obtain a text characteristic vector;
fusing the visual feature vector and the text feature vector to obtain a fused query image vector;
and performing similarity calculation between the fused query image vector and an image vector in a database to obtain a picture similar to the image to be queried.
Optionally, the step of performing visual feature extraction on the preprocessed image to be queried includes:
extracting each color channel of the preprocessed image to be processed, and generating adder mapping by using the local binary pattern of each channel pixel;
based on the adder map, the local binary pattern of the maximum channel for each pixel in the image is calculated, forming a texture histogram.
In one implementation, the expression of the visual feature vector is:
wherein, FFeatureDescriptor representing a textural feature of an image, FColorDescriptor, len (F), representing a color featureFeature) Texture feature descriptor lengthDegree and, len (F)Color) Representing the length of the color feature descriptor, imagesize (i) is the number of pixels in the input image.
Optionally, the step of extracting text information from the preprocessed image to be queried to obtain a text feature vector is as follows:
identifying a text in an image to be inquired through an open source optical character identification tool;
a text feature vector is obtained based on the recognized text.
The invention also provides a step of acquiring images in a database, comprising:
preprocessing each image to be placed in the database;
and performing visual feature extraction on each preprocessed image to obtain a visual feature extraction result corresponding to each image, wherein the visual feature extraction result corresponding to each image comprises: color features and texture features;
acquiring a visual feature vector corresponding to a visual feature extraction result of each image;
extracting text information of each image line, and using a text feature vector corresponding to the image;
and for each image, fusing the corresponding visual characteristic vector and text characteristic vector to obtain a fused image vector corresponding to the image, and storing the fused image vector in a database.
In addition, an image processing apparatus is disclosed, the apparatus comprising a processor, and a memory connected to the processor through a communication bus; wherein the content of the first and second substances,
the memory is used for storing an image processing program;
the processor is configured to execute the image processing program to implement the steps of any image processing method.
Also, a computer storage medium is disclosed, the computer storage medium storing one or more programs executable by one or more processors to cause the one or more processors to perform the steps of any of the image processing methods.
The image processing method, the image processing device and the computer storage medium provided by the embodiment of the invention have the following beneficial effects:
(1) the method calculates the LBP of a single pixel channel and integrates the LBP into the maximum multi-channel LBP (MMLBP) to obtain the inter-channel texture information of the color image, does not depend on the number of the channels of the color image, and solves the problem of dimension disaster.
(2) The invention fully utilizes the self text information of the image and the fusion of visual characteristics, and improves the retrieval accuracy.
Drawings
Fig. 1 is a schematic flow chart of an image processing method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of visual feature extraction according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.
Please refer to fig. 1-2. It should be noted that the drawings provided in the present embodiment are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The present invention provides an image processing method as shown in fig. 1, comprising:
s110, preprocessing an image to be queried;
it should be noted that, before indexing and searching, preprocessing needs to be performed on an image to be queried, for example, histogram equalization, binary filtering, and gamma conversion are performed on the image to be queried to achieve the effects of increasing the local contrast of the image, smoothing and removing dryness of the image, and enhancing the image, so as to facilitate feature extraction at a later stage, where a specific processing process is the prior art, and details are not repeated in the embodiments of the present invention.
S120, performing visual feature extraction on the preprocessed image to be inquired to obtain a visual feature extraction result, wherein the visual feature extraction result comprises: color features and texture features;
it can be understood that the visual feature of the preprocessed image is extracted, and in the whole visual feature extraction process, the color feature and the texture feature of the image to be queried are respectively extracted.
Specifically, the color information of each pixel is extracted by quantizing the RGB color space, and a color histogram is calculated, wherein the RGB color space has the characteristics of low calculation complexity and strong robustness. In this color space, the R, G, B channels are uniformly quantized to Q, respectivelyr、QG、QBThe unit shown. Color features of a single image that quantizes the entire color space from 0 to Qr*QG*QBQ of-1r*QG*QBThe color, defined as:
QRGB＝Qr*QG*R+QB*G+B
here, Qr、QGAnd QBIs set to 4 in order to quantize the resulting color space with 64 different intensity values from 0 to 63.
Further, extracting each color channel of the image, generating a big mapper by using the local binary pattern of each channel pixel, calculating the local binary pattern LBP of the maximum multichannel of each pixel in the image based on the mapping of the adder, and forming a texture histogram. Let I be a multichannel image of size P × Q × C, where P, Q, C represents the number of rows, columns, and channels of the image, respectively. I iscIs the C channel in I, C is ∈ [1, C]. Is provided with N neighborhoods and any pixel Ic(a, b) are equally radially spaced and are shown as
wherein N is an element of [1, N ∈]，a∈[1，P]，b∈[1，Q]，
To calculate a weight function for the generated LBP equivalent decimal. The adder map is defined as:
after calculating the MMLBP of each image, the texture features of the image are represented as a histogram, which is obtained by finding the frequency of occurrence of MMLBP values in the image, and is defined as FTexture＝h(rk)＝nkWherein r iskIs the MMLBP value of the pixel in the generated image, the value is 0,255]And nk is the number of pixels with the value MMLBP. As shown in fig. 2.
S130, obtaining a visual feature vector based on the visual feature extraction result;
it should be noted that the color feature and the texture feature are extracted based on the visual feature, and the formed color feature vector and the formed texture feature vector are fused into a single visual feature vector. The fusion formula is as follows:
wherein, FFeatureDescriptor representing a textural feature of an image, FColorDescriptor, len (F), representing a color featureFeature) Length sum of texture feature descriptor, len (F)Color) Representing the length of the color feature descriptor, imagesize (i) is the number of pixels in the input image.
S140, extracting text information of the preprocessed image to be inquired to obtain a text characteristic vector;
it can be understood that the text in the image to be queried is recognized by the existing open source optical character recognition OCR tool, and the recognized text words are used as labels and keywords to index the image, so as to form text feature vectors.
S150, fusing the visual feature vector and the text feature vector to obtain a fused query image vector;
it should be noted that feature fusion is a process of combining two feature vectors into one feature vector which is more discriminative than a single input feature vector. The feature fusion can be realized at a certain level, such as a matching level, a feature level and a decision level. The invention considers adopting a feature level fusion method because 2 data modes, namely the visual feature vector and the text feature vector, are generated. According to this fusion method, features extracted from the input entities are first combined and then further processed as a single unit for final fusion analysis. The mutual representation between two random feature vectors is processed using the canonical correlation analysis method (CCA). The purpose of CCA is to find two sets of projection directions to maximize the correlation of two eigenvectors after projection. The two feature vectors are finally expressed as:
wherein X and Y are respectively expressed as visual characteristic vector and text characteristic vector, WxAnd WyRepresenting a pair of projection directions on X and Y.
And S160, carrying out similarity calculation between the fused query image vector and the image vector in the database to obtain a picture similar to the image to be queried.
The invention also provides a step of acquiring images in a database, comprising:
preprocessing each image to be placed in the database;
and performing visual feature extraction on each preprocessed image to obtain a visual feature extraction result corresponding to each image, wherein the visual feature extraction result corresponding to each image comprises: color features and texture features;
acquiring a visual feature vector corresponding to a visual feature extraction result of each image;
extracting text information of each image line, and using a text feature vector corresponding to the image;
and for each image, fusing the corresponding visual characteristic vector and text characteristic vector to obtain a fused image vector corresponding to the image, and storing the fused image vector in a database.
The processing steps of the image vector in the database and the image vector to be queried are the same, specifically, the processing procedures in steps S110 to S150, which are not described herein again in the embodiments of the present invention.
The invention uses Euclidean distance to calculate the distance between the image to be inquired and the image stored in the database, then sorts the distance according to the ascending order of the obtained distance, outputs the image most similar to the image to be inquired, and then measures the image retrieval effect after fusion through common indexes such as precision, recall ratio and the like. The Euclidean distance calculation formula is as follows:
wherein the content of the first and second substances,
feature vectors representing database images, FqA feature vector representing the image to be queried.
It is understood that the calculated euclidean distance represents the similarity between the two images, and then the maximum value can be selected from the values of the euclidean distance, and the image corresponding to the maximum value can be used as the image with the highest similarity to the image to be queried.
In addition, a threshold value can be set, and when the euclidean distance is greater than the threshold value, all corresponding images are obtained and are the most similar to the image to be queried.
In addition, an image processing apparatus is disclosed, the apparatus comprising a processor, and a memory connected to the processor through a communication bus; wherein the content of the first and second substances,
the memory is used for storing an image processing program;
the processor is configured to execute the image processing program to implement the steps of any image processing method.
Also, a computer storage medium is disclosed, the computer storage medium storing one or more programs executable by one or more processors to cause the one or more processors to perform the steps of any of the image processing methods. The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (7)
1. An image processing method, comprising:
preprocessing an image to be queried;
performing visual feature extraction on the preprocessed image to be inquired to obtain a visual feature extraction result, wherein the visual feature extraction result comprises: color features and texture features;
obtaining a visual feature vector based on the visual feature extraction result;
extracting text information of the preprocessed image to be inquired to obtain a text characteristic vector;
fusing the visual feature vector and the text feature vector to obtain a fused query image vector;
and performing similarity calculation between the fused query image vector and an image vector in a database to obtain a picture similar to the image to be queried.
2. The image processing method according to claim 1, wherein the step of performing visual feature extraction on the preprocessed image to be queried comprises:
extracting each color channel of the preprocessed image to be processed, and generating adder mapping by using the local binary pattern of each channel pixel;
based on the adder map, the local binary pattern of the maximum channel for each pixel in the image is calculated, forming a texture histogram.
3. An image processing method according to claim 2, wherein the expression of the visual feature vector is:
wherein the content of the first and second substances,FFeaturedescriptor representing a textural feature of an image, FColorDescriptor, len (F), representing a color featureFeature) Length sum of texture feature descriptor, len (F)Color) Representing the length of the color feature descriptor, imagesize (i) is the number of pixels in the input image.
4. The image processing method according to claim 1, wherein the step of extracting text information from the preprocessed image to be queried to obtain the text feature vector comprises:
identifying a text in an image to be inquired through an open source optical character identification tool;
a text feature vector is obtained based on the recognized text.
5. An image processing method according to claim 1, wherein the step of obtaining the image vector in the database comprises:
preprocessing each image to be placed in the database;
and performing visual feature extraction on each preprocessed image to obtain a visual feature extraction result corresponding to each image, wherein the visual feature extraction result corresponding to each image comprises: color features and texture features;
acquiring a visual feature vector corresponding to a visual feature extraction result of each image;
extracting text information of each image line, and using a text feature vector corresponding to the image;
and for each image, fusing the corresponding visual characteristic vector and text characteristic vector to obtain a fused image vector corresponding to the image, and storing the fused image vector in a database.
6. An image processing apparatus, characterized in that the apparatus comprises a processor, and a memory connected to the processor through a communication bus; wherein the content of the first and second substances,
the memory is used for storing an image processing program;
the processor for executing the image processing program to implement the steps of the image processing method according to any one of claims 1 to 5.
7. A computer storage medium, characterized in that the computer storage medium stores one or more programs executable by one or more processors to cause the one or more processors to perform the steps of the image processing method according to any one of claims 1 to 6.
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