Content based image retrieval system with a combination of rough set and support vector machine

被引：0

作者：

Lotfabadi, Maryam Shahabi ^{[1
]}

Shiratuddin, Mohd Fairuz ^{[1
]}

Wong, Kok Wai ^{[1
]}

机构：

[1] School of Engineering and Information Technology, Murdoch University, 90 South Street, Murdoch, 6150, WA

来源：

Lecture Notes in Electrical Engineering | 2015年 / 312卷

关键词：

Classifier; Content based image retrieval system; Rough set; Support vector machine;

D O I：

10.1007/978-3-319-06764-3_20

中图分类号：

学科分类号：

摘要：

In this paper, a classifier based on a combination of Rough Set and 1-v-1 (one-versus-one) Support Vector Machine for Content Based Image Retrieval system is presented. Some problems of 1-v-1 Support Vector Machine can be reduced using Rough Set. With Rough Set, a 1-v-1 Support Vector Machine can provide good results when dealing with incomplete and uncertain data and features. In addition, boundary region in Rough Set can reduce the error rate. Storage requirements are reduced when compared to the conventional 1-v-1 Support Vector Machine. This classifier has better semantic interpretation of the classification process. We compare our Content Based Image Retrieval system with other image retrieval systems that uses neural network, K-nearest neighbour and Support Vector Machine as the classifier in their methodology. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the proposed method can retrieve images more efficiently than other methods in comparison. © Springer International Publishing Switzerland 2015.

引用

页码：157 / 163

页数：6

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