Applying multi-class SVMs into scene image classification

Authors:
Jianfeng Ren;Yuntao Shen;Songhui Ma;Lei Guo
Affiliations:
Department of Automatic Control, North Western Polytechnic University, Xi'an, China;Department of Automatic Control, North Western Polytechnic University, Xi'an, China;Department of Automatic Control, North Western Polytechnic University, Xi'an, China;Department of Automatic Control, North Western Polytechnic University, Xi'an, China
Venue:
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Year:
2004

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Abstract

Grouping images into semantically meaningful categories using the low-level visual features is a challenging and important problem in content-based image retrieval and other applications. In this paper, we show a specific high-level classification problem (scene images classification) using the low level features such as representative colors and Gabor textures. Based on the low level features, we introduce the multi-class SVMs to merge these features with the final goal to classify the different scene images. Experimental results show our method is promising.