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A Multi-Scale Learning Framework for Visual Categorization

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Computer Vision – ACCV 2010 (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6492))

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Abstract

Spatial pyramid matching has recently become a promising technique for image classification. Despite its success and popularity, no prior work has tackled the problem of learning the optimal spatial pyramid representation for the given image data and the associated object category. We propose a Multiple Scale Learning (MSL) framework to learn the best weights for each scale in the pyramid. Our MSL algorithm would produce class-specific spatial pyramid image representations and thus provide improved recognition performance. We approach the MSL problem as solving a multiple kernel learning (MKL) task, which defines the optimal combination of base kernels constructed at different pyramid levels. A wide range of experiments on Oxford flower and Caltech-101 datasets are conducted, including the use of state-of-the-art feature encoding and pooling strategies. Finally, excellent empirical results reported on both datasets validate the feasibility of our proposed method.

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Author information

Authors and Affiliations

  1. Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan

    Shao-Chuan Wang & Yu-Chiang Frank Wang

Authors
  1. Shao-Chuan Wang
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  2. Yu-Chiang Frank Wang
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Technion, Israel Institute of Technology, 32000, Haifa, Israel

    Ron Kimmel

  2. The University of Auckland, 37 Kohimarama Road, 1071, Mission Bay, Auckland, New Zealand

    Reinhard Klette

  3. National Institute of Informatics, 1018430, Chiyoda, Tokyo, Japan

    Akihiro Sugimoto

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Wang, SC., Wang, YC.F. (2011). A Multi-Scale Learning Framework for Visual Categorization. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19315-6_24

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  • DOI: https://doi.org/10.1007/978-3-642-19315-6_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19314-9

  • Online ISBN: 978-3-642-19315-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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