Image classification method based on concise unsupervised convolutional network

An unsupervised, convolutional network technology, applied in the field of image processing and deep learning, can solve the problems of high complexity of deep convolutional neural network models, large number of parameters, and strict requirements for labeled image data

Active Publication Date: 2015-11-11
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] In order to overcome the problems of high complexity of traditional deep convolutional neural network models, large number of parameters, difficulty in training network models, and strict requirements for labeled image data, this invention studies how to use a simple unsupervised algorithm to reduce The complexity of the network model, at the same time, a large number of unlabeled images can be used to train the network model

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  • Image classification method based on concise unsupervised convolutional network
  • Image classification method based on concise unsupervised convolutional network
  • Image classification method based on concise unsupervised convolutional network

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[0029] The specific implementation steps adopted by the present invention to solve its technical problems are as follows:

[0030] Step 1: The training image set Each training picture in is divided into multiple image blocks of size w×h, and the pixel composition dimension of each image block is R M The vector, where M=w×h×d, d represents the channel value of the image, for RGB pictures, d=3, for grayscale pictures, d=1; the entire training image set contains T image blocks in total, all These T image block vectors form a matrix P={p 1 ,...,p t ,...,p T}, where, t=1,...,T,p t ∈ R M ;

[0031] Step 2: Preprocessing the T image blocks;

[0032] Normalize according to formula (1), and whiten according to formula (2)(3)(4):

[0033] p ‾ t = p t - m e a n ...

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Abstract

The present invention provides an image classification method based on a concise unsupervised convolutional network, and belongs to the image processing and deep learning technology field. The method of the present invention utilizes a classic unsupervised clustering algorithm K-means to cluster the image blocks of a training image set, the obtained each clustering center is a convolution kernel in a network model, and the time-consuming process in the conventional convolutional network of utilizing a stochastic gradient descent algorithm repeatedly to obtain the convolution kernels is abandoned. In addition, the present invention provides a probability pooling method to enhance the robustness of the network to the image deformation. By a concise unsupervised depth convolutional network classification model provided by the present invention, the model training time can be shortened effectively, at the same time, the identification capability of the model to the changeable scene pictures is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing and deep learning, and relates to efficient image classification processing, in particular to an implementation scheme of image classification based on a concise unsupervised convolutional network. Background technique [0002] In recent years, image classification has received extensive attention and applications in the fields of industry, manufacturing, military, and medical treatment. Although its development situation is very good, as the coverage of practical applications gradually expands, massive image data will follow. Both the scale of the image database and the diversity of image content have reached an unprecedented peak. This makes the traditional Image processing methods are overwhelmed. Faced with such a large amount of image information, how to accurately classify images has become a research hotspot in related fields today. [0003] In the field of pattern recognition, t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/66
CPCG06V30/194G06F18/2411
Inventor 董乐张宁
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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