Quick image classification method based on deep learning

A technology of deep learning and classification methods, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of long training time, difficult training, low classification accuracy, etc., to speed up the classification speed, speed up the network convergence speed, The effect of ensuring classification accuracy

Active Publication Date: 2017-10-24
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to solve the problems of long training time, difficult training and low classification accuracy in the existing image classification technology, t

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  • Quick image classification method based on deep learning
  • Quick image classification method based on deep learning

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[0033] In order to better illustrate the technical solutions of the present invention, the present invention will be further described through an embodiment with reference to the accompanying drawings.

[0034] Reference figure 1 with figure 2 , A fast image classification method based on deep learning, the method includes four processes: deep convolutional neural network construction, data set preprocessing, network training and image classification testing.

[0035] The pictures in this implementation case are divided into 100 categories, and there are 600 pictures in each category. In each type of pictures, 500 pictures are randomly selected for training, and the remaining 100 pictures are used for testing. The image classification process is as figure 1 As shown, its structural framework is as figure 2 As shown, the operation steps include network construction, training process and testing process.

[0036] The fast image classification method includes the following steps:

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Abstract

The invention discloses a quick image classification method based on deep learning, and the method comprises the following steps: 1, network building; 2, data set preprocessing; 3, network training; 4, image classification, wherein the image classification comprises the following substeps: 4.1, inputting a testing data set into the trained network model after preprocessing, and extracting the multi-scale features of a testing image; 4.2, inputting the extracted multi-scale features into a Softmax classifier, and outputting the probability that the testing image belongs to one class; 4.3, inputting probability that the testing image belongs to one class and a label corresponding to the image into an Accuracy network layer, and outputting the probability that the image is classified correctly. Through the above steps, the method can achieve the quick classification of the testing image. The method is short in training time, is convenient in training, and is high in classification precision.

Description

technical field [0001] The present invention relates to multimedia big data processing and analysis in the field of computer vision, in particular to a fast image classification method based on deep learning, which belongs to the field of image classification. Background technique [0002] As a representative method of deep learning, convolutional neural network can automatically learn image feature extraction and reduce training parameters through weight sharing. It has a good effect in most computer vision tasks. However, the convolutional neural networks currently used for computer vision tasks tend to obtain more accurate image feature information by increasing the network depth, which not only increases network parameters, but also requires longer training time. [0003] In recent years, with the increasing amount of information available on the Internet, image classification on large data sets is not optimistic not only in terms of time cost but also computational cost...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24
Inventor 白琮黄玲陈佳楠郝鹏翼潘翔陈胜勇
Owner ZHEJIANG UNIV OF TECH
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