WO2020006961A1 - Procédé et dispositif d'extraction d'image - Google Patents

Procédé et dispositif d'extraction d'image Download PDF

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Publication number
WO2020006961A1
WO2020006961A1 PCT/CN2018/116334 CN2018116334W WO2020006961A1 WO 2020006961 A1 WO2020006961 A1 WO 2020006961A1 CN 2018116334 W CN2018116334 W CN 2018116334W WO 2020006961 A1 WO2020006961 A1 WO 2020006961A1
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image
matched
feature vector
position information
training
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PCT/CN2018/116334
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English (en)
Chinese (zh)
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周恺卉
王长虎
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北京字节跳动网络技术有限公司
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Publication of WO2020006961A1 publication Critical patent/WO2020006961A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures

Definitions

  • Embodiments of the present application relate to the field of computer technology, and in particular, to a method and an apparatus for extracting an image.
  • image recognition models to identify images is a common method of image recognition technology.
  • An image recognition model is usually a model that is trained using a large number of training samples.
  • a target image such as a watermark image, a person image, an object image, etc.
  • the sample images are trained to obtain an image recognition model.
  • the embodiments of the present application provide a method and a device for extracting an image.
  • an embodiment of the present application provides a method for extracting an image.
  • the method includes: obtaining a reference object image and a set of images to be matched; and inputting the reference object image into a first subnetwork included in a pre-trained image recognition model.
  • the to-be-matched images in the to-be-matched image set perform the following extraction steps: input the to-be-matched image into a second sub-network included in the image recognition model, and obtain at least one position information The feature vector to be matched corresponding to the position information, wherein the feature vector to be matched is the feature vector of the region image included in the image to be matched, and the position information is used to characterize the position of the region image in the to-be-matched image; The distance between the matching feature vector and the reference feature vector; in response to determining that there is a distance less than or equal to a preset distance threshold in the determined distance, the image to be matched is extracted as an image matching the reference object image.
  • the extracting step further includes: determining position information of an area image corresponding to a distance less than or equal to a distance threshold, and outputting the determined position information.
  • the extracting step further includes: generating a matched image including a position marker based on the output position information and the image to be matched, where the position marker is used to mark the image of the region to be matched corresponding to the output position information during matching. Position in the back image.
  • the second sub-network includes a dimension transformation layer for transforming the feature vector to a target dimension; and inputting the image to be matched into the second sub-network included in the image recognition model to obtain at least one feature vector to be matched,
  • the method includes: inputting the to-be-matched image into a second sub-network included in the image recognition model to obtain at least one to-be-matched feature vector having the same dimension as the reference feature vector.
  • the image recognition model is obtained by training in the following steps: obtaining a training sample set, where the training samples include sample object images, sample matching images, and labeled position information of the sample matching images.
  • the labeled position information indicates that the sample matching image includes Position of the regional image; select training samples from the training sample set, and perform the following training steps: input the sample object images included in the selected training samples into the first subnetwork included in the initial model, obtain the first feature vector, and input the sample matching images
  • the second sub-network included in the initial model obtains at least one position information and a second feature vector corresponding to the position information; and from the obtained at least one position information, the position information representing the target region image in the sample matching image is determined as a target Position information, and determine the second feature vector corresponding to the target position information as the target second feature vector; based on the first loss value representing the error of the target position information and the second feature vector representing the distance between the target second feature vector and the first feature vector Two loss values, Whether training is complete initial model; in response to determining
  • determining whether the initial model training is completed based on the first loss value representing the error of the target position information and the second loss value representing the difference between the distance between the target second feature vector and the first feature vector includes: The preset weight value uses the weighted summation result of the first loss value and the second loss value as the total loss value, and compares the total loss value with the target value, and determines whether the initial model is completed according to the comparison result.
  • the step of training to obtain an image recognition model further includes: in response to determining that the initial model is not trained, adjusting parameters of the initial model, and selecting training samples from unselected training samples in the training sample set, Use the adjusted initial model as the initial model and continue with the training steps.
  • an embodiment of the present application provides an apparatus for extracting an image.
  • the apparatus includes: an acquiring unit configured to acquire a reference object image and a set of images to be matched; and a generating unit configured to input the reference object image.
  • the first sub-network included in the pre-trained image recognition model obtains the feature vector of the reference object image as the reference feature vector;
  • the extraction unit is configured to perform the following extraction steps on the to-be-matched images in the to-be-matched image set:
  • the matching image is input to a second sub-network included in the image recognition model to obtain at least one position information and a feature vector corresponding to the position information, where the feature vector to be matched is a feature vector of an area image included in the image to be matched.
  • the matched image is an image that matches the reference target image.
  • the extraction unit includes: an output module configured to determine position information of a region image corresponding to a distance less than or equal to a distance threshold, and output the determined position information.
  • the extraction unit further includes a generating module configured to generate a matched image including a position marker based on the output position information and the image to be matched, where the position marker is used to mark the output position information corresponding to The position of the image to be matched in the matched image.
  • the second sub-network includes a dimensional transformation layer for transforming the feature vector to a target dimension; and the extraction unit is further configured to: input the image to be matched into the second sub-network included in the image recognition model to obtain At least one feature vector to be matched having the same dimension as the reference feature vector.
  • the image recognition model is obtained by training in the following steps: obtaining a training sample set, where the training samples include sample object images, sample matching images, and labeled position information of the sample matching images.
  • the labeled position information indicates that the sample matching image includes Position of the regional image; select training samples from the training sample set, and perform the following training steps: input the sample object images included in the selected training samples into the first subnetwork included in the initial model, obtain the first feature vector, and input the sample matching images
  • the second sub-network included in the initial model obtains at least one position information and a second feature vector corresponding to the position information; and from the obtained at least one position information, the position information representing the target region image in the sample matching image is determined as a target Position information, and determine the second feature vector corresponding to the target position information as the target second feature vector; based on the first loss value representing the error of the target position information and the second feature vector representing the distance between the target second feature vector and the first feature vector Two loss values, It is determined whether the initial model is trained; in response to
  • determining whether the initial model training is completed based on the first loss value representing the error of the target position information and the second loss value representing the difference between the distance between the target second feature vector and the first feature vector includes: The preset weight value uses the weighted summation result of the first loss value and the second loss value as the total loss value, and compares the total loss value with the target value, and determines whether the initial model is completed according to the comparison result.
  • the step of training to obtain an image recognition model further includes: in response to determining that the initial model is not trained, adjusting parameters of the initial model, and selecting training samples from unselected training samples in the training sample set, Use the adjusted initial model as the initial model and continue with the training steps.
  • an embodiment of the present application provides an electronic device.
  • the electronic device includes: one or more processors; a storage device on which one or more programs are stored; when one or more programs are read by one or more Each processor executes such that one or more processors implement the method as described in any implementation of the first aspect.
  • an embodiment of the present application provides a computer-readable medium having stored thereon a computer program that, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
  • the method and device for extracting images obtained by the embodiments of the present application obtain a reference feature vector of a reference image and at least one feature vector to be matched of an image to be matched by using a pre-trained image recognition model, and then compare the reference feature vector and The distance of the feature vector to be matched is used to obtain an image matching the reference image, so that when the training sample required for training the image recognition model does not include the reference image, the image recognition model is used to extract the image that matches the reference image, improving It increases the flexibility of image recognition and enriches the means of image recognition.
  • FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present application can be applied;
  • FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present application can be applied;
  • FIG. 2 is a flowchart of an embodiment of a method for extracting an image according to the present application
  • FIG. 3 is a flowchart of an image recognition model obtained by training of a method for extracting an image according to the present application
  • FIG. 4 is a schematic diagram of an application scenario of a method for extracting an image according to the present application
  • FIG. 5 is a flowchart of still another embodiment of a method for extracting an image according to the present application.
  • FIG. 6 is a schematic structural diagram of an embodiment of an apparatus for extracting an image according to the present application.
  • FIG. 7 is a schematic structural diagram of a computer system suitable for implementing an electronic device according to an embodiment of the present application.
  • FIG. 1 illustrates an exemplary system architecture 100 to which a method for extracting an image or an apparatus for extracting an image of an embodiment of the present application can be applied.
  • the system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105.
  • the network 104 is a medium for providing a communication link between the terminal devices 101, 102, 103 and the server 105.
  • the network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
  • the user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like.
  • Various communication client applications can be installed on the terminal devices 101, 102, and 103, such as image processing applications, shooting applications, social platform software, and the like.
  • the terminal devices 101, 102, and 103 may be hardware or software.
  • the terminal devices 101, 102, and 103 can be various electronic devices with a display screen, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.
  • the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it can be implemented as a single software or software module. It is not specifically limited here.
  • the server 105 may be a server that provides various services, such as a background server that provides support for various applications on the terminal devices 101, 102, and 103.
  • the background server may perform processing such as analysis on the acquired image, and output the processing result (for example, the extracted image matching the reference image).
  • the method for extracting an image provided in the embodiment of the present application may be executed by the server 105, or may be executed by the terminal devices 101, 102, and 103.
  • the device for extracting an image may be provided in the server 105 or in the terminal devices 101, 102, 103.
  • the server may be hardware or software.
  • the server can be implemented as a distributed server cluster consisting of multiple servers or as a single server.
  • the server can be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it can be implemented as a single software or software module. It is not specifically limited here.
  • terminal devices, networks, and servers in FIG. 1 are merely exemplary. Depending on the implementation needs, there can be any number of terminal devices, networks, and servers.
  • a flowchart 200 of one embodiment of a method for extracting an image according to the present application is shown.
  • the method for extracting an image includes the following steps:
  • Step 201 Obtain a reference object image and a set of images to be matched.
  • an execution subject of the method for extracting an image may obtain a reference object image and a set of images to be matched remotely or locally through a wired connection or a wireless connection.
  • the reference object image may be an image to be compared with other images, and the reference object image may be an image characterizing an object.
  • Objects can be various things, such as watermarks, signs, faces, objects, and so on.
  • the set of images to be matched may be a set of certain types of images (for example, images containing a trademark) stored in advance.
  • Step 202 Input a reference object image into a first sub-network included in a pre-trained image recognition model, and obtain a feature vector of the reference object image as a reference feature vector.
  • the execution subject may input the reference object image into a first sub-network included in a pre-trained image recognition model, and obtain a feature vector of the reference object image as the reference feature vector.
  • the first sub-network is used to characterize the correspondence between the image and the feature vector of the image.
  • the image recognition model may be various neural network models created based on machine learning technology.
  • the neural network model may have a structure of various neural networks (for example, DenseBox, VGGNet, ResNet, SegNet, etc.).
  • the above reference feature vector may be a feature (e.g., shape, color, texture) extracted from a first sub-network included in a neural network model (e.g., a network composed of one or some convolutional layers included in the neural network model), which characterizes an image And other characteristics).
  • a feature e.g., shape, color, texture
  • a neural network model e.g., a network composed of one or some convolutional layers included in the neural network model
  • Step 203 For the to-be-matched images in the to-be-matched image set, perform the following extraction steps: input the to-be-matched images into a second sub-network included in the image recognition model to obtain at least one position information and a feature vector to be matched corresponding to the location information; Determine the distance between the obtained feature vector to be matched and the reference feature vector; and in response to determining that there is a distance less than or equal to a preset distance threshold in the determined distance, extract the to-be-matched image as an image matching the reference object image.
  • the execution subject may perform the following extraction step on the to-be-matched image:
  • Step 2031 Input the image to be matched into the second sub-network included in the image recognition model, and obtain at least one location information and a feature vector to be matched corresponding to the location information.
  • the second sub-network is used to characterize the correspondence between the image and the position information of the image and the feature vector to be matched of the image.
  • the position information is used to characterize the position of the area image corresponding to the feature vector to be matched in the to-be-matched image.
  • the feature vector to be matched is a feature vector of an area image included in the image to be matched.
  • the second sub-network may determine each position information from the images to be matched according to the determined at least one position information. Characterize the region images, and determine the feature vector of each region image.
  • the area image may be an image characterizing an object (eg, a watermark, a logo, etc.).
  • the location information may include coordinate information and identification information. Among them, the coordinate information (such as the coordinates of the corner points of the regional image, the size of the regional image, etc.) is used to indicate the position of the regional image in the image to be matched, and the identification information (such as the serial number of the regional image and the category of the regional image) is used to identify the area image.
  • an image to be matched includes two watermarked images
  • the position information determined by the second subnet is "(1, x1, y1, w1, h1)" and (2, x2, y2, w2, h2)
  • 1, 2 are the serial numbers of the two watermarked images
  • (x1, y1), (x2, y2) are the coordinates of the upper left corner of the two watermarked images
  • w1 and w2 are the widths of the two watermarked images.
  • H1, h2 are the heights of the two watermark images, respectively.
  • the execution subject can extract feature vectors of the to-be-matched images, and extract feature vectors corresponding to the two position information as feature-to-be-matched feature vectors from the feature vectors of the to-be-matched images, respectively.
  • the second sub-network may be a neural network based on an existing target detection network (for example, SSD (Single Shot MultiBox Detector), R-CNN (Region-based Convolutional Neural Networks, Faster R-CNN, etc.).
  • SSD Single Shot MultiBox Detector
  • R-CNN Registered-based Convolutional Neural Networks
  • Faster R-CNN etc.
  • the foregoing second sub-network includes a dimension transformation layer for transforming a feature vector to a target dimension.
  • the dimension transformation layer may process the feature vector (for example, the values of some dimensions included in the feature vector are combined by taking an average value); it may also be a pooling layer included in the second sub-network.
  • the pooling layer can be used to down-sample or up-sample the input data to compress or increase the amount of data.
  • the above target dimensions may be various dimensions set by a technician, for example, the same dimensions as those of the reference feature vector.
  • the execution subject may input the image to be matched into a second sub-network included in the image recognition model, and the second sub-network extracts at least one feature vector of the image to be matched, and then extracts the dimensional transformation layer pair included in the second sub-network.
  • Each feature vector of the dimensional transform is performed to obtain at least one feature vector to be matched that has the same dimension as the reference feature vector.
  • a ROI Pooling (Region Of Interest, Pooling) layer can be used, so that each feature vector to be matched has the same dimension as the reference feature vector.
  • the ROI Pooling layer is a well-known technology that is widely studied and applied at present, and will not be repeated here.
  • Step 2032 Determine the distance between the obtained feature vector to be matched and the reference feature vector.
  • the execution body may determine a distance between each of the obtained at least one to-be-matched feature vector and a reference feature vector.
  • the distance may be any of the following: Euclidean distance, Mahalanobis distance, and the like.
  • the preset distance may be any value greater than or equal to 0.
  • the distance can represent the degree of similarity between two feature vectors, that is, the degree of similarity between two images. As an example, if the distance between the two feature vectors is larger, the images that are corresponding to the two feature vectors are less similar.
  • Step 2033 In response to determining that a distance smaller than or equal to a preset distance threshold exists in the determined distance, extract the image to be matched as an image matching the reference object image.
  • the distance threshold may be a value set by a technician according to experience, or may be a value calculated (for example, by calculating an average value) calculated by the execution subject based on historical data (for example, a recorded historical distance threshold). Specifically, if there is a distance smaller than the distance threshold in each of the determined distances, it indicates that an area image similar to the reference object image exists in the image to be matched, that is, the image to be matched matches the reference object image.
  • the image recognition model can be obtained by training in advance through the following steps:
  • Step 301 Obtain a training sample set.
  • the training samples include sample object images, sample matching images, and sample matching image labeling position information.
  • the labeling position information represents the position of the region image included in the sample matching image.
  • the sample object image may be an image characterizing an object (for example, a watermark, a logo, a human face, an object, etc.).
  • the number of labeled position information may be at least one, and each labeled information may correspond to a region image, and each region image includes a region image in which the characterized object and the sample object image are the same.
  • Step 302 Select training samples from the training sample set.
  • the selection method and the number of training samples are not limited in this application.
  • the training samples may be selected from the training sample set in a random selection or in the order of the number of training samples.
  • Step 303 input the sample object image included in the selected training sample into the first sub-network included in the initial model to obtain a first feature vector, and input the sample matching image into the second sub-network included in the initial model to obtain at least one position information and the The second feature vector corresponding to the position information.
  • the initial model may be various existing neural network models created based on machine learning technology.
  • the neural network model may have various existing neural network structures (for example, DenseBox, VGGNet, ResNet, SegNet, etc.).
  • Each of the above feature vectors may be a vector composed of data extracted from certain layers (such as a convolution layer) included in the neural network model.
  • the above-mentioned first sub-network and second sub-network are the same as the first sub-network and the second sub-network described in step 202 and step 203, respectively, and details are not described herein again.
  • Step 304 From the obtained at least one position information, determine position information representing the target region image in the sample matching image as the target position information, and determine a second feature vector corresponding to the target position information as the target second feature vector.
  • the above-mentioned target area image may be an area image in which the characterized object is the same as the object characterized by the sample object image.
  • the execution subject performing this step may use the position information specified by the technician as the target position information, the area image characterized by the target position information as the target area image, and the second feature vector of the target area image as the target second feature vector; or The execution subject executing this step may determine the similarity between the area image corresponding to each position information and the sample object image and determine the area image with the greatest similarity to the sample object image as the target area image according to the obtained position information. , Determining the position information of the target area image as the target position information, and determining the second feature vector of the target area image as the target second feature vector.
  • Step 305 Determine whether the initial model training is completed based on the first loss value representing the error of the target position information and the second loss value representing the difference between the distance between the target second feature vector and the first feature vector.
  • the first loss value may represent a gap between the target position information and the labeled position information corresponding to the target area image.
  • the smaller the first loss value the smaller the difference between the target position information and the labeled position information corresponding to the target area image, that is, the closer the target position information is to the labeled position information.
  • the first loss value can be obtained according to any of the following loss functions: Softmax loss function, Smooth L1 (smooth L1 norm) loss function, and the like.
  • the second loss value may represent a distance between the target second feature vector and the first feature vector.
  • the larger the second loss value the greater the distance between the target second feature vector and the first feature vector, that is, the less similar the target area image and the sample object image.
  • the second loss value may be a distance between the target second feature vector and the first feature vector (eg, Euclidean distance, Mahalanobis distance, etc.).
  • the second loss value can be obtained by the Triplet loss function, where the Triplet error function is as follows:
  • L is the second loss value
  • ⁇ is the sum sign
  • i is the serial number of each training sample selected this time
  • a represents the sample object image
  • p represents the positive sample image (that is, the target area image).
  • n represents a negative sample image (that is, an area image other than the target area image in the sample matching image; or a preset, characterized image where the object is different from the object characterized by the sample object image).
  • a feature vector representing a sample object image included in the training sample with the sequence number i A feature vector representing a positive sample image (such as an image of a target region) corresponding to the training sample with the number i, Characterize the feature vector of a negative sample image (for example, a region image other than the target region image in the sample matching image) corresponding to the training sample with the number i.
  • threshold represents a preset distance
  • Characterizing the first distance that is, the distance between the first feature vector and the feature vector of the positive sample image
  • Represent the first distance that is, the distance between the first feature vector and the feature vector of the negative sample image).
  • the "+" on the lower right side of the square brackets in the above formula means taking a positive value, that is, when the expression of the expression in the square brackets is positive, the positive value is taken, and when it is negative, 0 is taken.
  • the parameters of the initial model can be adjusted according to the back-propagation algorithm so that the L value is minimum or the L value converges, indicating that the training is complete.
  • the execution subject performing this step may obtain the total loss value based on the first loss value and the second loss value, compare the total loss value with the target value, and determine whether the initial model is completed according to the comparison result.
  • the target value may be a preset loss value threshold. When the difference between the total loss value and the target value is less than or equal to the loss value threshold, it is determined that the training is completed.
  • an executing subject executing this step may use a weighted summation result of the first loss value and the second loss value as a total loss value according to a preset weight value, and use the total loss value.
  • the loss value is compared with the target value, and whether the initial model is trained is determined based on the comparison result.
  • the above weight value can adjust the ratio of the first loss value and the second loss value to the total loss value, so that the image recognition model can achieve different functions in different application scenarios (for example, some scenes focus on extracting position information, and some scenes focus on Compare distances of feature vectors).
  • Step 306 In response to determining that the training is completed, determine the initial model as an image recognition model.
  • the execution subject of the image recognition model that is trained may respond to determining that the initial model is not trained, adjusting the parameters of the initial model, and unselected training samples from the training sample set.
  • a training sample is selected, and an initial model adjusted by parameters is used as an initial model, and the training step is continued.
  • the initial model is a convolutional neural network
  • the backpropagation algorithm can be used to adjust the weights in each convolutional layer in the initial model.
  • a training sample may be selected from the unselected training samples in the training sample set, and the initial model adjusted by parameters is used as the initial model, and steps 303 to 306 are continuously performed.
  • the execution subject of the image recognition model obtained through the training may be the same as or different from the execution subject of the method for extracting an image. If they are the same, the training subject who has obtained the image recognition model may store the structure information and parameter values of the parameters of the trained image recognition model locally after obtaining the image recognition model. If they are different, the execution subject trained in the image recognition model may send the structure information and parameter values of the trained image recognition model to the execution subject of the method for extracting the image after the image recognition model is trained.
  • FIG. 4 is a schematic diagram of an application scenario of a method for extracting an image according to this embodiment.
  • the server 401 first obtains a watermark image 402 (ie, a reference object image) uploaded by the terminal device 408, and obtains a set of images 403 to be matched locally.
  • the server 401 inputs the watermark image 402 into the first sub-network 4041 included in the pre-trained image recognition model 404, and obtains a feature vector of the watermark image 402 as a reference feature vector 405.
  • the server 401 selects one to-be-matched image 4031 from the to-be-matched image set 403, inputs the to-be-matched image 4031 into the second sub-network 4041 included in the image recognition model 404, and obtains the position information 4061, 4062, and 4063, and the corresponding position information.
  • the feature vectors to be matched 4071, 4072, and 4073 are the feature vectors of the watermark images 40311, 40312, and 40313 included in the image 4031 to be matched, respectively.
  • the server 401 determines that the distance between the feature vector to be matched 4071 and the reference feature vector 405 is less than or equal to a preset distance threshold, extracts the to-be-matched image 4031 as an image matching the reference object image, and sends the matched image to the terminal device 408.
  • the server 401 repeatedly selects the image to be matched from the set of images to be matched 403 and the watermarked image 402 to match, thereby extracting multiple images from the set of images to be matched 403 that match the watermarked image 402.
  • a pre-trained image recognition model is used to obtain a reference feature vector of a reference image and at least one feature vector to be matched of the image to be matched, and then compare the reference feature vector and the feature vector to be matched.
  • Distance to obtain an image matching the reference image thereby improving the pertinence of matching with the reference image, and realizing that when the training samples required for training the image recognition model do not include the reference image, the image recognition model is used to extract the The reference image matches the image, which improves the flexibility of image recognition and enriches the means of image recognition.
  • a flowchart 500 of still another embodiment of a method for extracting an image is shown.
  • the process 500 of the method for extracting an image includes the following steps:
  • Step 501 Obtain a reference object image and a set of images to be matched.
  • step 501 is substantially the same as step 501 in the embodiment corresponding to FIG. 2, and details are not described herein again.
  • Step 502 Input a reference object image into a first sub-network included in a pre-trained image recognition model, and obtain a feature vector of the reference object image as a reference feature vector.
  • step 502 is substantially the same as step 502 in the embodiment corresponding to FIG. 2, and details are not described herein again.
  • Step 503 for the to-be-matched images in the to-be-matched image set, perform the following extraction step: input the to-be-matched images into a second sub-network included in the image recognition model to obtain at least one position information and a feature vector to be matched corresponding to the position information; Determining the distance between the obtained feature vector to be matched and the reference feature vector; in response to determining that there is a distance less than or equal to a preset distance threshold in the determined distance, extracting the image to be matched as an image matching the reference object image; Position information of a region image corresponding to a distance equal to a distance threshold, and output the determined position information.
  • the execution subject may perform the following extraction step on the to-be-matched image:
  • Step 5031 Input the image to be matched into the second sub-network included in the image recognition model to obtain at least one position information and a feature vector to be matched corresponding to the position information.
  • Step 5031 is basically the same as step 2031 in the embodiment corresponding to FIG. 2, and details are not described herein again.
  • Step 5032 Determine the distance between the obtained feature vector to be matched and the reference feature vector. Step 5032 is basically the same as step 2032 in the embodiment corresponding to FIG. 2, and details are not described herein again.
  • Step 5033 In response to determining that a distance smaller than or equal to a preset distance threshold exists in the determined distance, extract the image to be matched as an image matching the reference object image. Step 5033 is basically the same as step 2033 in the embodiment corresponding to FIG. 2, and details are not described herein again.
  • Step 5034 Determine the position information of the area image corresponding to the distance less than or equal to the distance threshold, and output the determined position information.
  • an execution subject of the method for extracting an image may be obtained from step 5031 based on the distance determined in step 5032 and equal to or less than a preset distance threshold.
  • position information corresponding to a distance less than or equal to the distance threshold is determined, and position information corresponding to a distance less than or equal to the distance threshold is output.
  • the execution subject may output position information in various ways.
  • the display body connected to the execution subject may display information such as identification information, coordinate information, and the like of a region image included in the position information.
  • the above-mentioned execution subject may generate a matched image including a position mark based on the output location information and the image to be matched after outputting the location information.
  • the position marker is used to mark the position of the region to be matched image corresponding to the output position information in the matched image.
  • the execution subject may draw a frame of a preset shape in the image to be matched according to the output position information, use the drawn frame as a position mark, and use the to-be-matched image including the position mark as a matched image.
  • the process 500 of the method for extracting an image in this embodiment highlights the steps of outputting position information. Therefore, the solution described in this embodiment can further determine the position of the target region image included in the image to be matched, and improve the specificity of image recognition.
  • this application provides an embodiment of an apparatus for extracting an image.
  • the apparatus embodiment corresponds to the method embodiment shown in FIG. 2.
  • the device can be specifically applied to various electronic devices.
  • the apparatus 600 for extracting an image in this embodiment includes: an acquiring unit 601 configured to acquire a reference object image and a set of images to be matched; and a generating unit 602 configured to input a reference object image into a pre-training A first sub-network included in the image recognition model of FIG.
  • an extraction unit 603 is configured to perform the following extraction step on the to-be-matched images in the to-be-matched image set:
  • the image is input to a second sub-network included in the image recognition model to obtain at least one position information and a feature vector to be matched corresponding to the position information, where the feature vector to be matched is a feature vector of an area image included in the image to be matched, and the position information is used for Characterizing the position of the area image in the image to be matched; determining the distance between the obtained feature vector to be matched and the reference feature vector; and in response to determining that a distance less than or equal to a preset distance threshold exists in the determined distance, extracting the to be matched
  • the image is an image matching the reference target image.
  • the obtaining unit 601 may obtain the reference object image and the set of images to be matched from a remote or local source through a wired connection method or a wireless connection method.
  • the reference object image may be an image to be compared with other images, and the reference object image is an image representing an object.
  • Objects can be various things, such as watermarks, signs, faces, objects, and so on.
  • the set of images to be matched may be a set of certain types of images (for example, images containing a trademark) stored in advance.
  • the generating unit 602 may input the reference object image into a first sub-network included in a pre-trained image recognition model, and obtain a feature vector of the reference object image as a reference feature vector.
  • the first sub-network is used to characterize the correspondence between the image and the feature vector of the image.
  • the image recognition model may be various neural network models created based on machine learning technology.
  • the neural network model may have a structure of various neural networks (for example, DenseBox, VGGNet, ResNet, SegNet, etc.).
  • the above reference feature vector may be a feature (e.g., shape, color, texture) extracted from a first sub-network included in a neural network model (e.g., a network composed of one or some convolutional layers included in the neural network model), which characterizes an image And other characteristics).
  • a feature e.g., shape, color, texture
  • a neural network model e.g., a network composed of one or some convolutional layers included in the neural network model
  • the extraction unit 603 may perform the following steps on the image to be matched:
  • the image to be matched is input to a second sub-network included in the image recognition model, and at least one position information and a feature vector corresponding to the position information are obtained.
  • the second sub-network is used to characterize the correspondence between the image and the position information of the image and the feature vector to be matched of the image.
  • the position information is used to characterize the position of the area image corresponding to the feature vector to be matched in the to-be-matched image.
  • the feature vector to be matched is a feature vector of an area image included in the image to be matched.
  • a distance between the obtained feature vector to be matched and the reference feature vector is determined.
  • the above-mentioned extraction unit 603 may determine a distance between each of the obtained at least one feature vector to be matched and the reference feature vector.
  • the distance may be any of the following: Euclidean distance, Mahalanobis distance, and the like.
  • the image to be matched is extracted as an image matching the reference object image.
  • the distance threshold may be a value set by a technician based on experience, or may be a value calculated (for example, by calculating an average value) calculated by the extraction unit 603 according to historical data (for example, a recorded historical distance threshold).
  • the extraction unit 603 may include an output module configured to determine position information of a region image corresponding to a distance less than or equal to a distance threshold, and output the determined position information.
  • the extraction unit 603 may further include: a generating module configured to generate a matched image including a position marker based on the output position information and the image to be matched, where the position marker It is used to mark the position of the region to be matched image corresponding to the output position information in the matched image.
  • a generating module configured to generate a matched image including a position marker based on the output position information and the image to be matched, where the position marker It is used to mark the position of the region to be matched image corresponding to the output position information in the matched image.
  • the second sub-network may include a dimension transformation layer for transforming a feature vector to a target dimension; and the extraction unit 603 may be further configured to: input the image to be matched into an image Identify the second sub-network included in the model, and obtain at least one feature vector to be matched that has the same dimension as the reference feature vector.
  • the image recognition model is obtained by training in the following steps: obtaining a training sample set, where the training sample includes labeled object information of the sample object image, the sample matched image, and the sample matched image, and the labeled position
  • the information characterizes the position of the region image included in the sample matching image; selects training samples from the training sample set, and executes the following training steps: inputting the sample object image included in the selected training sample into the first sub-network included in the initial model to obtain the first feature Vector, and input the sample matching image into the second sub-network included in the initial model to obtain at least one location information and a second feature vector corresponding to the location information; and determine the target characterizing the sample matching image from the obtained at least one location information
  • the position information of the area image is used as the target position information
  • the second feature vector corresponding to the target position information is determined as the target second feature vector; based on the first loss value representing the error of the target position information and the target feature second feature vector and the first feature Distance of vector Loss
  • the execution subject of the image recognition model trained to obtain the weighted summation result of the first loss value and the second loss value as the total loss value according to a preset weight value and The total loss value is compared with the target value, and whether the initial model is trained is determined based on the comparison result.
  • the step of training to obtain an image recognition model may further include: in response to determining that the initial model is not trained, adjusting parameters of the initial model, and selecting unselected data from the training sample set. Among the training samples, a training sample is selected, and the initial model adjusted by parameters is used as the initial model, and the training step is continued.
  • the apparatus obtains a reference feature vector of a reference image and at least one feature vector to be matched of an image to be matched by using a pre-trained image recognition model, and then compares the reference feature vector and the feature vector to be matched by Distance to obtain an image matching the reference image, thereby improving the pertinence of matching with the reference image, and realizing that when the training samples required for training the image recognition model do not include the reference image, the image recognition model is used to extract the
  • the reference image matches the image, which improves the flexibility of image recognition and enriches the means of image recognition.
  • FIG. 7 illustrates a schematic structural diagram of a computer system 700 suitable for implementing an electronic device (such as a server or a terminal device shown in FIG. 1) in the embodiment of the present application.
  • an electronic device such as a server or a terminal device shown in FIG. 1
  • the electronic device shown in FIG. 7 is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present application.
  • the computer system 700 includes a central processing unit (CPU) 701, which can be based on a program stored in a read-only memory (ROM) 702 or a program loaded from a storage section 708 into a random access memory (RAM) 703. Instead, perform various appropriate actions and processes.
  • ROM read-only memory
  • RAM random access memory
  • various programs and data required for the operation of the system 700 are also stored.
  • the CPU 701, ROM 702, and RAM 703 are connected to each other through a bus 704.
  • An input / output (I / O) interface 705 is also connected to the bus 704.
  • the following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc .; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc .; and a speaker; And a communication section 709 including a network interface card such as a LAN card, a modem, and the like.
  • the communication section 709 performs communication processing via a network such as the Internet.
  • the driver 710 is also connected to the I / O interface 705 as needed.
  • a removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read out therefrom is installed into the storage section 708 as needed.
  • the process described above with reference to the flowchart may be implemented as a computer software program.
  • embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable medium, the computer program containing program code for performing a method shown in a flowchart.
  • the computer program may be downloaded and installed from a network through the communication section 709, and / or installed from a removable medium 711.
  • CPU central processing unit
  • the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable medium or any combination of the foregoing.
  • the computer-readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable Read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • a computer-readable medium may be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal that is included in baseband or propagated as part of a carrier wave, and which carries computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium can also be any computer-readable medium other than a computer-readable medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Computer program code for performing the operations of this application may be written in one or more programming languages, or a combination thereof, including programming languages such as Java, Smalltalk, C ++, and also conventional Procedural programming language—such as "C" or a similar programming language.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer, partly on a remote computer, or entirely on a remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider) Internet connection).
  • LAN local area network
  • WAN wide area network
  • Internet service provider Internet service provider
  • each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, which contains one or more functions to implement a specified logical function Executable instructions.
  • the functions labeled in the blocks may also occur in a different order than those labeled in the drawings. For example, two blocks represented one after the other may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts can be implemented by a dedicated hardware-based system that performs the specified function or operation , Or it can be implemented with a combination of dedicated hardware and computer instructions.
  • the units described in the embodiments of the present application may be implemented by software or hardware.
  • the described unit may also be provided in a processor, for example, it may be described as: a processor includes an acquisition unit, a generation unit, and an extraction unit. Wherein, the names of these units do not constitute a limitation on the unit itself in some cases, for example, the obtaining unit may also be described as a “unit for obtaining a reference target image and an image set to be matched”.
  • the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device in.
  • the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: obtains a reference object image and a set of images to be matched; and inputs the reference object image into a pre-trained
  • the first sub-network included in the image recognition model obtains the feature vector of the reference object image as the reference feature vector; for the to-be-matched images in the to-be-matched image set, the following extraction step is performed: input the to-be-matched image into the first Two sub-networks to obtain at least one location information and a feature vector corresponding to the location information, wherein the feature vector to be matched is a feature vector of an area image included in the image to be matched, and the location information is used to characterize the area image in the to-be-matched image.

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Abstract

La présente invention concerne, selon des modes de réalisation, un procédé et un dispositif d'extraction d'image. Un mode de réalisation selon le procédé consiste : à acquérir une image d'un objet de référence et un ensemble d'images à mettre en correspondance ; et à entrer l'image d'objet de référence dans un premier sous-réseau compris dans un modèle de reconnaissance d'image pré-appris, et à acquérir un vecteur de caractéristique de l'image d'objet de référence en tant que vecteur de caractéristique de référence, les étapes d'extraction suivantes étant exécutées sur des images à mettre en correspondance dans l'ensemble d'images à mettre en correspondance : l'entrée de l'image à mettre en correspondance dans un deuxième sous-réseau compris dans le modèle de reconnaissance d'image de manière acquérir au moins des informations de position et un vecteur de caractéristique à mettre en correspondance correspondant aux informations de position ; la détermination de la distance entre le vecteur de caractéristique acquis à mettre en correspondance et le vecteur de caractéristique de référence ; et l'extraction, en fonction de la détermination que les distances déterminées comprennent une distance inférieure ou égale à un seuil de distance prédéfini, de l'image à mettre en correspondance en tant que l'image correspondant à l'image d'objet de référence. Les modes de réalisation de l'invention permettent d'améliorer la flexibilité de la reconnaissance d'image et d'enrichir les moyens de la reconnaissance d'image.
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