Image recognition by embedded hopffeld SVM

被引：0
|
作者：
Zhao, Zhonghua [1 ]
Xin, Haiyan [2 ]
Liu, Tao [3 ]
机构：
[1] Department of Communication and Information Engineering, Guilin University of Electronic Technology, Guilin 541004, China
[2] Department of Electronic Engineering, Guilin University of Aerospace Technology, Guilin 541004, China
[3] Institute of Information Technology, Guilin University of Electronic Technology, Guilin 541004, China
来源：
Journal of Computational Information Systems | 2012年 / 8卷 / 17期
关键词：
Image retrieval - Principal component analysis - Vectors - Neural networks - Support vector machines - Image recognition;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Hopfield support vector machine has a higher stable performance than traditional support vector machine, which can be used as a powerful tool to solve the classification problems.Thus, image recognition based on embedded hopfield support vector machine is proposed in this paper. First, principal component analysis technology is used to extract the feature of image, and every row of two dimensions grey matrix is cascaded to form a vector of one dimension, the vector is the image feature data, which is used to training image recognition models. Then, we can use 6 SVMs, hopfield SVMs to recognize the 7 classes' images respectively. The experimental results demonstrate that the recognition ability of embedded hopfield SVM is obviously better than that of the traditional SVM and BP neural network. © 2012 Binary Information Press.
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页码：7311 / 7316
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