Image processing method and apparatus, and computer-readable storage medium

An image processing apparatus, comprising:

• a memory for storing computer-readable instructions;
• and
• a processor for executing the computer-readable instructions to cause the image processing apparatus to perform an image processing method, said method comprising:
• inputting an image into an optimized neural network;
• extracting, by the optimized neural network, image features of the image;
• and
• outputting the image features,
• wherein the optimized neural network is obtained by performing a first optimization process on at least one sub-layer in a pre-trained initial neural network, each sub-layer of the at least one sub-layer includes a convolutional layer, and the first optimization process comprises:
• for each sub-layer of the at least one sub-layer,
• determining one or more channels to be removed from a filter of the convolutional layer and removing said one or more channels, and optimizing parameters of remaining channels in the filter of the convolutional layer, so that error of output features of each optimized sub-layer is minimized.

A computer-readable storage medium for storing computer-readable instructions capable of, when being executed by a computer, causing the computer to perform an image processing method comprising:

• inputting an image into an optimized neural network;
• extracting, by the optimized neural network, image features of the image;
• and
• outputting the image features,
• wherein the optimized neural network is obtained by performing a first optimization process on at least one sub-layer in a pre-trained initial neural network, each sub-layer of the at least one sub-layer includes a convolutional layer, and the first optimization process comprises:
• for each sub-layer of the at least one sub-layer,
• determining one or more channels to be removed from a filter of the convolutional layer and removing said one or more channels, and optimizing parameters of remaining channels in the filter of the convolutional layer, so that error of output features of each optimized sub-layer is minimized.