Image classification method based on convolution neural network

Abstract

The invention discloses an image classification method based on a convolution neural network. The method comprises the following steps: constructing a deep convolution neural network; improving the deep convolution neural network; training and testing the deep convolution neural network; and optimizing the network parameter. By using the image classification method disclosed by the invention, the improvement and the optimization are respectively performed on the network structure and multiple parameters of the convolution neural network, the recognition rate of the deep convolution neural network can be effectively improved, and the accuracy of the image classification is improved.

Description

technical field [0001] The present invention relates to the technical field of image classification methods, in particular to an image classification method based on a convolutional neural network. Background technique [0002] In recent years, with the development of scientific computer networks and artificial intelligence, the amount of graphic and image data has gradually increased. Therefore, how to quickly extract visual features from a large number of natural images has become a hot research topic in machine intelligence learning. Furthermore, the classification of natural images must become the research focus of acquiring natural image information. [0003] Convolutional neural network is an important application of deep learning in image processing. Compared with other machine learning algorithms such as SVM, its advantage is that it can directly convolve image pixels and extract features, and can also use massive image data. Fully train the network parameters to ac...

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Problems solved by technology

[0004] The main purpose of the present invention is to provide an image classification method based on convolutional neur

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