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Image classification method based on convolution neural network

A technology of convolutional neural network and classification method, which is applied in the direction of neural learning method, biological neural network model, instrument, etc., and can solve problems such as unsatisfactory classification effect of complex image data

Pending Publication Date: 2017-11-10
EAST CHINA UNIV OF TECH
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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 neural network, aiming to solve the problem that the prior art has unsatisfactory classification effect on complex image data

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  • Image classification method based on convolution neural network
  • Image classification method based on convolution neural network
  • Image classification method based on convolution neural network

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Embodiment Construction

[0104] In order to make the technical means, creative features, goals and functions realized by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0105] An embodiment of the present invention provides an image classification method based on a convolutional neural network, including the following steps: construction of a deep convolutional neural network, improvement of a deep convolutional neural network, training and testing of a deep convolutional neural network, and optimization of the network parameter;

[0106] 1. Construction of deep convolutional neural network: On the basis of ImageNet-2010 network, a deep convolutional neural network with nine layers of network is designed by analyzing the network layer by layer, such as figure 1 As shown, it includes image input layer, conv1 convolution layer, conv2 convolution layer, conv3 convolution layer, conv4 convolution layer, conv5 convo...

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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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Application Information

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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/084G06F18/2415
Inventor 王蕾何月顺王坤蒋年德钟国韵蔡友林
Owner EAST CHINA UNIV OF TECH
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