文章基本信息

标题：A Convolutional Neural Network Image Classification Based on Extreme Learning Machine
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作者：Shasha Wang ; Daohua Liu ; Zhipeng Yang 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：3
语种：English
出版社：IAENG - International Association of Engineers
摘要：To improve the classification accuracy of the massive amount of image data, we propose a novel extreme learning machine classification model for image classification which based on convolutional neural networks. Based on the framework of Alex Net network, the hash function is constructed as the hidden layer between image representation and classification output in convolutional neural network. At the same time, the extreme learning machine is introduced at the end of the network layer, which saves the classification time, improves the classification efficiency and further improves the feature expression ability of the network. Through comparative experiments in the standard databases MNIST and CIFAR10, the effects of various improved methods under different situations are analyzed. The experimental results show that the proposed convolutional neural network image classification method based on the extreme learning machine improves the average precision by 3%-31% compared with other image classification methods in this paper.
关键词：image classification;convolutional neural network;extreme learning machine;hash coding.