CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of the filing date of each of Chinese Patent Application No. 201811155326.6 filed on Sep. 30, 2018, Chinese Patent Application No. 201811155252.6 filed on Sep. 30, 2018, Chinese Patent Application No. 201811155147.2 filed on Sep. 30, 2018, and Chinese Patent Application No. 201811155930.9 filed on Sep. 30, 2018, the disclosure of each of which is hereby incorporated in its entirety by reference.

TECHNICAL FIELD

The present disclosure generally relates to the field of deep learning technologies, and more particularly, to a system, method, and computer-readable medium for deep learning-based image classification.

BACKGROUND

Deep learning technology on the basis of an artificial neural network has made great progress in such fields as image processing. An advantage of the deep learning technology lies in the solution of different technical problems using an all-purpose structure and a relatively similar system.

BRIEF SUMMARY

An embodiment of the present disclosure is a cascaded system for classifying an image. The cascaded system may comprise a first cascade layer comprising a first analysis module coupled to a first input terminal, and a first pooling module coupled to the first analysis module; a second cascade layer comprising a second analysis module coupled to a second input terminal, and a second pooling module coupled to the first pooling module and the second analysis module; a synthesis layer coupled to the second pooling module; and an activation layer coupled to the synthesis layer.

In some embodiments, the first cascade layer may be configured to receive a first input image having a first input resolution R1, inputvia the first analysis module and generate a first output image via the first pooling module based on the first input image. The second cascade layer may be configured to receive a second input image having a second input resolution R2, inputvia the second analysis module and the first output image from the first cascade layer, and generate a second output image via the second pooling module. The synthesis layer may be configured to generate a training image based on the second output image. The activation layer may be configured to apply an activation function to the training image, and classify the training image based on the comparison with the predetermined standard image.

In some embodiments, the first input resolution and the second input resolution have the following relationship: R1, input>R2, input. In some embodiments, each of the first output image and the second output image may have a lower resolution than the corresponding first input image and second input image, respectively.

In some embodiments, R1, input=2R2, input.

In some embodiments, The first output image may have a first output resolution R1, output, and the second output image has a second output resolution R2, output, and R1, output=½R1, input, and R2, output=½R2, input.

In some embodiments, each of the first analysis module and the second analysis module may be configured to generate a feature image of a corresponding input image.

In some embodiments, the feature image may have a larger number of color channels than the corresponding input image.

In some embodiments, each of the first analysis module and the second analysis module may comprise a first convolution layer configured to perform a convolution on the corresponding input image to generate an intermediate feature image, and a first filter configured to apply a first filter to the intermediate feature image to generate the feature image.

In some embodiments, the first pooling module may be configured to concatenate a first feature image generated by the first analysis module with itself to generate a first composite image, and downsample the first composite image to generate a first downsampled composite image. The second pooling module may be configured to concatenate the second input image with the first output image to generate a second composite image, and downsample the second composite image to generate a second downsampled composite image. The first downsampled composite and the second downsampled composite image may be the first, and second output images, respectively.

In some embodiments, each of the first, and second downsampled composite images may have the same number of color channels as the corresponding feature image.

In some embodiments, each of the first, and second pooling modules may comprise a connector configured to perform the concatenating, a second convolution layer configured to downsample a corresponding composite image to generate an intermediate downsampled composite image, and a second filter configured to apply a second filter to the intermediate downsampled composite image to generate the downsampled composite image.

In some embodiments, the training image may have a smaller number of color channels than the second output image.

In some embodiments, the synthesis module may comprise a third filter configured to apply a third filter to the third output image to generate an intermediate training image, and a fourth convolution layer configured to perform a convolution on the intermediate training image to generate the training image.

In some embodiments, the activation layer may comprise a sigmoid layer.

Another embodiment of the present disclosure is a method for classifying an image. The method may comprise identifying from a plurality of input images a first input image having a highest resolution; performing at least one downsampling operation on the first input image on a cascaded convolutional neural network; and classifying an output image generated by performing the at least one downsampling operations, based on a comparison with a predetermined standard image.

In some embodiments, the performing of the at least one downsampling operations may comprise: extracting one or more feature from the first input image to generate a first feature image of the first input image; concatenating the first feature image with itself to generate a first composite image; downsampling the first composite image to generate a first output image; extracting one or more feature from a second input image from among the plurality of input images to generate a second feature image; concatenating the second feature image with the first output image to generate a second composite image; and downsampling the second composite image to generate a second output image.

In some embodiments, the first output image may have a lower resolution than the first input image, and the same resolution as the second input image.

In some embodiments, the first input image may have a first input resolution R1, input, and the second input image has a second input resolution R2, input, and R1, input=2R2, input.

In some embodiments, the first output image may have a first output resolution, and the second output image has a second output resolution, and R1, output=½R1, input, and R2, output=½R2, input.

In some embodiments, the steps of extracting, concatenating, and downsampling may be repeated for each of the plurality of input images in sequence, and each subsequent one of the plurality of input images may have a lower resolution than a previous one of the plurality of input images.

In some embodiments, the classifying of the output image may comprise generating a training image based on the output image; and classifying the training image based on the comparison with the predetermined standard image.

Another embodiment of the present disclosure is a non-transitory computer-readable medium storing instructions that cause a computer to execute a method for classifying an image. The method may be as described above.

Another embodiment of the present disclosure is a system for training a generative adversarial network. The system may comprise a generative adversarial network processor, comprising a generative network microprocessor which is configured to be trained by a discriminative network microprocessor, and the discriminative network processor.

In some embodiments, the discriminative network processor may comprise a cascaded system for classifying an image. The cascaded system may be as described above.

