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Support Vector Machines Applications pp 155–189Cite as

Application of SVMs to the Bag-of-Features Model: A Kernel Perspective

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Abstract

The Bag-of-features model has recently achieved great success in image categorisation and become the state of the art. Support vector machines (SVMs) have played an important role in this process. This chapter first introduces the fundamentals of the Bag-of-features model in image categorisation. Following that, it is focused on how the SVM classifiers are applied to this model. In particular, we show the novel kernels developed to compare images based on a variety of representations incurred by this model. Also, how the kernels are implicitly implemented or effectively approximated to work with linear SVMs is discussed. Through this chapter, we will see that the application of SVMs not only demonstrates its elegance and efficiency but also raises new research issues to stimulate the development of SVMs.

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Author information

Authors and Affiliations

  1. University of Wollongong, Wollongong, NSW, 2522, Australia

    Lei Wang & Luping Zhou

  2. Australian National University, Acton, ACT, 0200, Australia

    Lingqiao Liu

  3. Nanyang Technological University, 639798, Singapore, Singapore

    Kap Luk Chan

Authors
  1. Lei Wang
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  2. Lingqiao Liu
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  3. Luping Zhou
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  4. Kap Luk Chan
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Corresponding author

Correspondence to Lei Wang .

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Editors and Affiliations

  1. Honeywell, Golden Valley, Minnesota, USA

    Yunqian Ma

  2. West Virginia University, Morgantown, West Virginia, USA

    Guodong Guo

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Wang, L., Liu, L., Zhou, L., Chan, K.L. (2014). Application of SVMs to the Bag-of-Features Model: A Kernel Perspective. In: Ma, Y., Guo, G. (eds) Support Vector Machines Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-02300-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-02300-7_5

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