METHOD OF GENERATING CLASSIFIER BY USING SMALL NUMBER OF LABELED IMAGES

Abstract

A method of generating a classifier by using a small number of labeled images includes: pre-training a wide residual network by using a set of labeled data with a data amount meeting requirements, and determining portions of the pre-trained wide residual network except for a fully connected layer as a feature extractor for an image; randomly selecting, for a N-class classifier to be generated, N classes from a training set for each of a plurality of times; and for N classes selected each time: randomly selecting one or more images from each class of the N classes as training samples; extracting a feature vector from training samples of each class by using the feature extractor; inputting a total of N feature vectors extracted into a classifier generator; and sequentially performing a class information fusion and a parameter prediction for the N-class classifier by using the classifier generator.

Description

TECHNICAL FIELD

The present disclosure relates to a field of artificial intelligence, and in particular to a method of generating a classifier by using a small number of labeled images.

BACKGROUND

Image classification is an image processing method that distinguishes different classes of objects according to their different features reflected in image information. Instead of human visual interpretation, the image classification uses a computer to perform a quantitative analysis on an image, and classifies the image or each pixel or region in the image into one of several classes.

At present, deep neural network-based classification methods are very mature, but most methods rely on massive amounts of labeled data. Furthermore, when the class of the image changes, these classification methods cannot adjust quickly, thereby affecting an effect of the image classification.

SUMMARY

An objective of the present disclosure is to provide a method of generating a classifier by using a small number of labeled images, so as to ensure accuracy of image classification.

In an aspect, the objective of the present disclosure is achieved by the following method.

There is provided a method of generating a classifier by using a small number of labeled images, including:

• • pre-training a wide residual network by using a set of labeled data with a data amount meeting requirements, and determining portions of the pre-trained wide residual network except for a fully connected layer as a feature extractor for an image;
  • randomly selecting, for a N-class classifier to be generated, N classes from a training set for each of a plurality of times; and
  • for N classes selected each of the plurality of times:
    • randomly selecting one or more images from the each class of the N classes as training samples;
    • extracting a feature vector from training samples of each class, by using the feature extractor;
    • inputting a total of N feature vectors extracted into a classifier generator; and
    • sequentially performing a class information fusion and a parameter prediction for the N-class classifier by using the classifier generator.

In another aspect, the objective of the present disclosure is achieved by the following computer device.

There is provided a computer device of generating a classifier by using a small number of labeled images, including:

• • a processor; and
  • a memory having instructions executable by the processor, wherein the instructions, when executed by the processor, cause the processor to:
    • pre-train a wide residual network by using a set of labeled data with a data amount meeting requirements, and determine portions of the pre-trained wide residual network except for a fully connected layer as a feature extractor for an image;
    • randomly select, for a N-class classifier to be generated, N classes from a training set for each of a plurality of times; and
    • for N classes selected each of the plurality of times:
      • randomly select one or more images from each class of the N classes as training samples;
      • extract a feature vector from training samples of each class, by using the feature extractor;
      • input a total of N feature vectors extracted into a classifier generator; and
      • sequentially perform a class information fusion and a parameter prediction for the N-class classifier by using the classifier generator.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the accompanying drawings required in the description of the embodiments are briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure. For those ordinary skilled in the art, other drawings may be obtained from these drawings without carrying out any inventive effort.

FIG. 1 shows a flowchart of a method of generating a classifier by using a small number of labeled images according to some embodiments of the present disclosure.

FIG. 2 shows a structural diagram of an apparatus of generating a classifier by using a small number of labeled images according to some embodiments of the present disclosure.

FIG. 3 shows a block diagram of a computer device of generating a classifier by using a small number of labeled images according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The technical solutions of the present disclosure are clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. Obviously, the embodiments described are only a part but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those ordinary skilled in the art without making inventive efforts fall within the protection scope of the present disclosure.

The embodiments of the present disclosure provide a method of generating a classifier by using a small number of labeled images. FIG. 1 shows a flowchart of a method 10 of generating a classifier by using a small number of labeled images according to some embodiments of the present disclosure. FIG. 2 shows a structural diagram of an apparatus 20 of generating a classifier by using a small number of labeled images according to some embodiments of the present disclosure. The flowchart of the method shown in FIG. 1 will be described below in conjunction with the structure shown in FIG. 2. As shown in FIG. 1, the method generally includes following steps.

In step S100, a wide residual network is pre-trained by using a set of labeled data with a data amount meeting requirements, and portions of the pre-trained wide residual network except for a fully connected layer are determined as a feature extractor 200 for an image.

In the embodiments of the present disclosure, the step S100 may include: at step S1001, a set of labeled data with a data amount meeting requirements is selected, and the set of labeled data is divided, according to image classes, into a training set and a test set that do not overlap each other; at step S1002, the wide residual network is trained for a predetermined number of times by using the training set; and at step S1003, the trained wide residual network is tested by using the test set.

The wide residual network includes a multi-layer convolutional neural network and a fully connected layer. In the pre-training process, after each image is input into the wide residual network, an output of the fully connected layer at the end of the wide residual network indicates a classification score of the input image being classified into each class.

In the pre-training process, a loss function is defined as:

$L=\sum _i-s_{i,y}+\log \left(\sum _{y\prime }{s}_{i,y\prime }\right)$

where s_i,y indicates a classification score of an i^th image to be classified being classified into a true class y in each batch training, and s_i,y′, indicates a classification score of the i^th image being classified into the other class y′.

After a certain number of training and passing the test of the test set, the pre-training of the wide residual network is completed.

When the pre-training is completed, portions of the wide residual network except for the fully connected layer are retained as a feature extractor 200 for the image.

In step S102, for an N-class classifier 206 to be generated, N classes are randomly selected from the training set for each of a plurality of times. For N classes selected each time, step S104 to step S106 are performed.

In step S104, one or more images are randomly selected from each class of the N classes as training samples.

In step S106, a feature vector is extracted from the training samples of each class by using the feature extractor 200.

In the embodiments of the present disclosure, step S106 may further include one of step S1061 and step S1062.

At step S1061, if an image is extracted from each class as a training sample, a feature vector is extracted from each training sample, so that a total of N feature vectors are finally extracted for N classes.

At step S1062, if a plurality of images are extracted from each class as training samples, a plurality of feature vectors are extracted from a plurality of training samples of each class, and an average of the plurality of feature vectors are determined as the feature vector for the class, so that a total of N feature vectors are finally extracted for N classes.

In step 5108, a total of N feature vectors extracted are input into a classifier generator.

In the embodiments of the present disclosure, a specific value of N may be set according to requirements, and a number of the images selected from each class may also be set according to experience or requirements.

In step S110, a class information fusion and a parameter prediction for the N-class classifier are sequentially performed by the classifier generator. After step S104 to step S100 are performed for each randomly selected N classes, the N-class classifier 206 is obtained.

The classifier generator in the embodiments of the present disclosure may include a class information fusion module 202 and a classifier parameter prediction module 204.

In the embodiments of the present disclosure, step S108 may further include: at S1081, the feature vectors for the N classes are stitched to form a matrix with N rows; and at S1082, the matrix is input into the class information fusion module 202 to obtain a fusion feature matrix. Each row of the fusion feature matrix indicates a class feature for a corresponding row of the input matrix.

The class information fusion module 202 includes a fully connected layer having N input dimensions and N output dimensions.

In the embodiments of the present disclosure, step S110 may further include: at S1101, the fusion feature matrix is input into the classifier parameter prediction module 204 to predict a parameter of the N-class classifier 206.

The classifier parameter prediction module 204 includes a fully connected layer having input and output dimensions same as dimension of the feature vector of the image.

For example, if the feature extractor 200 may obtain a 640-dimensional vector for each input image, the classifier parameter prediction module 204 may have 640-dimensional input and output. The classifier parameter prediction module 204 may predict the parameter of the classifier 206 according to the output of the class information fusion module 202. For example, an N×640-dimensional matrix may be obtained according to the previous assumption. The matrix is the final classifier parameter, a 640-dimensional image feature is finally input into the N classifier 206, and an N-dimensional classification score is obtained. The class with the highest score is determined as a predicted class.

In addition, in the embodiments of the present disclosure, the method shown in FIG. 1 may optionally further include step S112, in which the N-class classifier 206 is trained after it is obtained. The training step S112 may include following steps: at S112₁, a certain number of (for example, 15) images are randomly selected from each of N classes as images to be tested; at S112₂, feature vectors of the images to be tested are extracted by using the feature extractor 200; at S112₃, the extracted feature vectors are input directly into the N-class classifier to predict classification scores of the images being classified into each class; and at S112₄, the parameter of the N-class classifier is updated according to a result of the prediction.

A loss function used in the training process of the N-class classifier is as follows:

$L=\sum _i-s_{i,y}+\log \left(\sum _{y\prime }{s}_{i,y\prime }\right)$

where S_i,y indicates a classification score of an i^th image to be classified being classified into a true class y in each batch training, and s_i,y′, indicates a classification score of the i^th image being classified into the other class y′. The loss function is the same as that used in the pre-training of the wide residual network except for the number of the image classes involved.

In addition, when the training is completed, N new classes and one or more new samples of each class are given, and a new classifier may be directly generated to classify images of these N new classes. In particular, it may use only a sample for each new class during the training. In practice, if there are a plurality of available samples in a class, an average of the feature vectors of these samples may be used instead of the feature vector of a single sample.

The above solution according to the embodiments of the present disclosure is completely based on a 2D convolutional neural network. According to this solution, after training on a large training set, a classifier identifying new classes may be generated based on a small number of samples of the new classes. During the testing on a dedicated few-sample learning dataset, for the generation of a 5-class classifier (that is, N=5), when only one sample is used for each new class, the classification accuracy of the generated classifier may reach 60.04%, and when five samples are used for each new class, the classification accuracy may reach 74.15%.

FIG. 3 shows a block diagram of a computer device 30 of generating a classifier by using a small number of labeled images according to some embodiments of the present disclosure. As shown in FIG. 3, the computer device 30 includes a processor 31 and a memory 32. The memory 32 may store instructions executable by the processor 31. The instructions, when executed by the processor 31, cause the processor 31 to perform the steps of the method shown in FIG. 1

Through the description of the above embodiments, those skilled in the art may clearly understand that the above embodiments may be implemented by software, or may be implemented by means of software with a necessary general hardware platform. Based on this understanding, the technical solutions of the above embodiments may be embodied in the form of a software product. The software product may be stored in a non-volatile storage medium (which may be a CD-ROM, U disk, mobile hard disk, etc.). The non-volatile storage medium includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in various embodiments of the present disclosure.

The above-mentioned technical solutions of the present disclosure provide a method of generating a classifier. When classifying images of a new class, a new classifier may be generated by using a small number of images in the new class, so as to ensure the accuracy of image classification.

The above are only preferred specific implementations of the present disclosure, and the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that may be easily conceived by those skilled in the art within the technical scope disclosed in the present disclosure should be covered by the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be determined by the scope of protection defined by the claims.

Claims

1. A method of generating a classifier by using a small number of labeled images, comprising: pre-training a wide residual network by using a set of labeled data with a data amount meeting requirements, and determining portions of the pre-trained wide residual network except for a fully connected layer as a feature extractor for an image;randomly selecting, for a N-class classifier to be generated, N classes from a training set for each of a plurality of times; andfor N classes selected each of the plurality of times: randomly selecting one or more images from each class of the N classes as training samples;extracting a feature vector from training samples of each class, by using the feature extractor;inputting (S108) a total of N feature vectors extracted into a classifier generator; andsequentially performing (S110) a class information fusion and a parameter prediction for the N-class classifier by using the classifier generator.

2. The method of claim 1, wherein the pre-training a wide residual network by using a set of labeled data with a data amount meeting requirements comprises: selecting a set of labeled data with a data amount meeting requirements, and dividing the set of labeled data into a training set and a test set according to image classes, wherein the training set and the test set do not overlap each other;training the wide residual network for a predetermined number of times by using the training set; andtesting the trained wide residual network by using the test set,wherein the wide residual network comprises a multi-layer convolutional neural network and a fully connected layer; and in the pre-training process, after each image is input into the wide residual network, an output of the fully connected layer at the end of the wide residual network indicates a classification score of the input image being classified into each class; andwherein, in the pre-training process, a loss function is defined as:

3. The method of claim 1, wherein the extracting a feature vector from training samples of each class, by using the feature extractor comprises: extracting a feature vector from a training sample of each class in response to extracting an image from each class as the training sample, so that a total of N feature vectors are finally extracted for N classes; orextracting a plurality of feature vectors from a plurality of training samples of each class in response to extracting a plurality of images from each class as the training samples, and determining an average of the plurality of feature vectors as the feature vector for the each class, so that a total of N feature vectors are finally extracted for N classes.

4. The method of claim 1, wherein the classifier generator comprises a class information fusion module and a classifier parameter prediction module; and wherein the method further comprises: stitching feature vectors for the N classes to form a matrix with N rows;inputting the matrix into the class information fusion module so as to obtain a fusion feature matrix, wherein each row of the fusion feature matrix indicates a class feature for a corresponding row of the matrix input; andinputting the fusion feature matrix to the classifier parameter prediction module, so as to predict a parameter of the N-class classifier.

5. The method of claim 4, wherein the class information fusion module comprises a fully connected layer having N input dimensions and N output dimensions.

6. The method of claim 4, wherein the classifier parameter prediction module comprises a fully connected layer having input and output dimensions same as dimensions of the feature vector of the image.

7. The method of claim 1, further comprising: training the N-class classifier after the N-class classifier is obtained, comprising: randomly selecting a number of images from each class of the N classes as images to be tested;extracting feature vectors of the images to be tested by using the feature extractor;inputting the feature vectors extracted directly into the N-class classifier, so as to predict classification scores of the images to be tested being classified into each class; andupdating the parameter of the N-class classifier according to a result of the prediction, wherein a loss function used in the training process of the N-class classifier is the same as that used in the pre-training process of the wide residual network except for a number of image classes involved.

8. A computer device of generating a classifier by using a small number of labeled images, comprising: a processor; anda memory having instructions executable by the processor, wherein the instructions, when executed by the processor, cause the processor to: pre-train a wide residual network by using a set of labeled data with a data amount meeting requirements, and determine portions of the pre-trained wide residual network except for a fully connected layer as a feature extractor for an image;randomly select, for a N-class classifier to be generated, N classes from a training set for each of a plurality of times; andfor N classes selected each of the plurality of times: randomly select one or more images from each class of the N classes as training samples;extract a feature vector from training samples of each class, by using the feature extractor;input a total of N feature vectors extracted into a classifier generator; andsequentially perform a class information fusion and a parameter prediction for the N-class classifier by using the classifier generator.

9. The computer device of claim 8, wherein the instructions, when executed by the processor, further cause the processor to: select a set of labeled data with a data amount meeting requirements, and divide the set of labeled data into a training set and a test set according to image classes, wherein the training set and the test set do not overlap each other;train the wide residual network for a predetermined number of times by using the training set; andtest the trained wide residual network by using the test set;wherein the wide residual network comprises a multi-layer convolutional neural network and a fully connected layer; and in the pre-training process, after each image is input into the wide residual network, an output of the fully connected layer at the end of the wide residual network indicates a classification score of the input image being classified into each class, andwherein, in the pre-training process, a loss function is defined as:

10. The computer device of claim 8, wherein the instructions, when executed by the processor, further cause the processor to: extract a feature vector from a training sample of each class in response to extracting an image from each class as the training sample, so that a total of N feature vectors are finally extracted for N classes; orextract a plurality of feature vectors from a plurality of training samples of each class in response to extracting a plurality of images from each class as the training samples, and determine an average of the plurality of feature vectors as the feature vector for the each class, so that a total of N feature vectors are finally extracted for N classes.

11. The computer device of claim 8, wherein the classifier generator comprises a class information fusion module and a classifier parameter prediction module; and wherein the instructions, when executed by the processor, further cause the processor to: stitch feature vectors for the N classes to form a matrix with N rows;input the matrix into the class information fusion module so as to obtain a fusion feature matrix, wherein each row of the fusion feature matrix indicates a class feature for a corresponding row of the matrix input; andinput the fusion feature matrix into the classifier parameter prediction module, so as to predict a parameter of the N-class classifier.

12. The computer device of claim 11, wherein the class information fusion module comprises a fully connected layer having N input dimensions and N output dimensions.

13. The computer device of claim 11, wherein the classifier parameter prediction module comprises a fully connected layer having input and output dimensions same as dimensions of the feature vector of the image.

14. The computer device of any one of claim 8, wherein the instructions, when executed by the processor, further cause the processor to: train the N-class classifier after the N-class classifier is obtained, comprising: randomly select a number of images from each class of the N classes as images to be tested;extract feature vectors of the images to be tested by using the feature extractor;input the feature vectors extracted directly into the N-class classifier, so as to predict classification scores of the images to be tested being classified into each class; andupdate the parameter of the N-class classifier according to a result of the prediction, wherein a loss function used in the training process of the N-class classifier is the same as that used in the pre-training process of the wide residual network except for a number of image classes involved.

15. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, causes the processor to perform the method of claim 1.

16. The method of claim 2, further comprising: training the N-class classifier after the N-class classifier is obtained, comprising: randomly selecting a number of images from each class of the N classes as images to be tested;extracting feature vectors of the images to be tested by using the feature extractor;inputting the feature vectors extracted directly into the N-class classifier, so as to predict classification scores of the images to be tested being classified into each class; andupdating the parameter of the N-class classifier according to a result of the prediction, wherein a loss function used in the training process of the N-class classifier is the same as that used in the pre-training process of the wide residual network except for a number of image classes involved.

17. The method of claim 3, further comprising: training the N-class classifier after the N-class classifier is obtained, comprising: randomly selecting a number of images from each class of the N classes as images to be tested;extracting feature vectors of the images to be tested by using the feature extractor;inputting the feature vectors extracted directly into the N-class classifier, so as to predict classification scores of the images to be tested being classified into each class; andupdating the parameter of the N-class classifier according to a result of the prediction, wherein a loss function used in the training process of the N-class classifier is the same as that used in the pre-training process of the wide residual network except for a number of image classes involved.

18. The method of claim 4, further comprising: training the N-class classifier after the N-class classifier is obtained, comprising: randomly selecting a number of images from each class of the N classes as images to be tested;extracting feature vectors of the images to be tested by using the feature extractor;inputting the feature vectors extracted directly into the N-class classifier, so as to predict classification scores of the images to be tested being classified into each class; andupdating the parameter of the N-class classifier according to a result of the prediction, wherein a loss function used in the training process of the N-class classifier is the same as that used in the pre-training process of the wide residual network except for a number of image classes involved.