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Applying Multi-class SVMs into Scene Image Classification

  • Conference paper
Innovations in Applied Artificial Intelligence (IEA/AIE 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3029))

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.

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© 2004 Springer-Verlag Berlin Heidelberg

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Ren, J., Shen, Y., Ma, S., Guo, L. (2004). Applying Multi-class SVMs into Scene Image Classification. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_95

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  • DOI: https://doi.org/10.1007/978-3-540-24677-0_95

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22007-7

  • Online ISBN: 978-3-540-24677-0

  • eBook Packages: Springer Book Archive

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