The Algorithm Research of Image Classification Based on Deep Convolutional Network

Wu Daqin, Huang Haiyan
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引用次数: 2

Abstract

In order to solve large amount of images classification issues, a method is introduced by combining with Convolutional Neural Network(CNN) and IMVCl-10. Based on a large number of training data, the classification accuracy of the Convolution Neural Network can exceed the SVM classification accuracy. In this dataset, 3K and 100K batch data are used to train the depth convolution network, and the classification accuracy of 70% and more than 80% can be achieved.
基于深度卷积网络的图像分类算法研究
为了解决大量图像分类问题,提出了一种卷积神经网络(CNN)与IMVCl-10相结合的方法。基于大量的训练数据,卷积神经网络的分类精度可以超过支持向量机的分类精度。在该数据集中,分别使用3K和100K批数据对深度卷积网络进行训练,分类准确率分别达到70%和80%以上。
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