Applying multi-class SVMs into scene image classification

Jianfeng Ren; Yuntao Shen; Songhui Ma; Lei Guo

doi:10.1007/978-3-540-24677-0_95

Applying multi-class SVMs into scene image classification

Jianfeng Ren, Yuntao Shen, Songhui Ma, Lei Guo

School of Automation

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Conference article › peer-review

15 Scopus citations

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.

Original language	English
Pages (from-to)	924-934
Number of pages	11
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3029
DOIs	https://doi.org/10.1007/978-3-540-24677-0_95
State	Published - 2004
Event	17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada Duration: 17 May 2004 → 20 May 2004

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10.1007/978-3-540-24677-0_95

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Y2 - 17 May 2004 through 20 May 2004

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Applying multi-class SVMs into scene image classification

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