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http://dx.doi.org/10.3745/KIPSTB.2002.9B.6.759

A Machine Learning Approach to Web Image Classification  

Cho, Soo-Sun (한국전자통신연구원 정보가전연구부)
Lee, Dong-Woo (한국전자통신연구원 정보가전연구부)
Han, Dong-Won (한국전자통신연구원)
Hwang, Chi-Jung (충남대학교 컴퓨터과학과)
Publication Information
The KIPS Transactions:PartB / v.9B, no.6, 2002 , pp. 759-764 More about this Journal
Abstract
Although image occupies a large part of importance on the Web documents, there have not been many researches for analyzing and understanding it. Many Web images are used for carrying important information but others are not used for it. In this paper classify the Web images from presently served Web sites to erasable or non-erasable classes. based on machine learning methods. For this research, we have detected 16 special and rich features for Web images and experimented by using the Baysian and decision tree methods. As the results, F-measures of 87.09%, 82.72% were achived for each method and particularly, from the experiments to compare the effects of feature groups, it has proved that the added features on this study are very useful for Web image classification.
Keywords
Image Classification; Machine Learning; Features of Web Images; Bayes Classifier; Decision Tree;
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Times Cited By KSCI : 1  (Citation Analysis)
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