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Ambiguous Proximity Distribution

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Intelligent Computing Methodologies (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8589))

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Abstract

Proximity Distribution Kernel is an effective method for bag-of-featues based image representation. In this paper, we investigate the soft assignment of visual words to image features for proximity distribution. Visual word contribution function is proposed to model ambiguous proximity distributions. Three ambiguous proximity distributions is developed by three ambiguous contribution functions. The experiments are conducted on both classification and retrieval of medical image data sets. The results show that the performance of the proposed methods, Proximity Distribution Kernel (PDK), is better or comparable to the state-of-the-art bag-of-features based image representation methods.

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Author information

Authors and Affiliations

  1. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China

    Quanquan Wang & Yongping Li

  2. University of Chinese Academy of Sciences, Bejing, 100049, China

    Quanquan Wang

  3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

    Quanquan Wang

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  1. Quanquan Wang
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  2. Yongping Li
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Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680–749, Ulsan, Korea

    Kang-Hyun Jo

  3. Department of Automation, Tsinghua University, Bejing, China

    Ling Wang

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Wang, Q., Li, Y. (2014). Ambiguous Proximity Distribution. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_42

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  • DOI: https://doi.org/10.1007/978-3-319-09339-0_42

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09338-3

  • Online ISBN: 978-3-319-09339-0

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