A Machine Learning Approach to Web Image Classification


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 6, pp. 759-764, Jun. 2002
10.3745/KIPSTB.2002.9.6.759, Full Text:

Abstract

Although image occupies a large part of importance on the Web documemts, 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 we 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.


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Cite this article
[IEEE Style]
S. S. Cho, D. W. Lee, D. W. Han and C. J. Hwang, "A Machine Learning Approach to Web Image Classification," KIPS Journal B (2001 ~ 2012) , vol. 9, no. 6, pp. 759-764, 2002. DOI: 10.3745/KIPSTB.2002.9.6.759.

[ACM Style]
Soo Sun Cho, Dong Woo Lee, Dong Won Han, and Chi Jung Hwang. 2002. A Machine Learning Approach to Web Image Classification. KIPS Journal B (2001 ~ 2012) , 9, 6, (2002), 759-764. DOI: 10.3745/KIPSTB.2002.9.6.759.