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International Conference on Multimedia Modeling

MMM 2010: Advances in Multimedia Modeling pp 303–313Cite as

Image Matching Based on Representative Local Descriptors

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5916))

Abstract

While straightforward image matching with keypoint based local descriptors produces a high matching accuracy, it is usually accompanied by enormous computation load. In this paper we present a representative local descriptors (RLDs) based approach to improve image matching efficiency without sacrificing matching accuracy. Firstly, local descriptors in one image are clustered with a similarity based method where descriptors are clustered into one group if they are similar enough to their mean. Then only the RLD in each group is used in matching and the number of matched RLDs is used to evaluate the similarity of two images. Experiments indicate that the RLDs approach produces better matching accuracy than both straightforward matching with original descriptors and visual words matching.

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Author information

Authors and Affiliations

  1. School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China

    Jian Hou, Naiming Qi & Jianxin Kang

  2. School of Engineering, Northeast Agriculture University, Harbin, China, 150030

    Jianxin Kang

Authors
  1. Jian Hou
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  2. Naiming Qi
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  3. Jianxin Kang
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Editor information

Editors and Affiliations

  1. University of Oldenburg, Germany

    Susanne Boll

  2. University of Texas at San Antonio,, TX, San Antonio, USA

    Qi Tian

  3. Microsoft Research Asia, Beijing, P.R. China

    Lei Zhang

  4. Southwest University, Beibei, Chongqing, China

    Zili Zhang

  5. School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, Vic, 3125, Australia

    Yi-Ping Phoebe Chen

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© 2010 Springer-Verlag Berlin Heidelberg

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Hou, J., Qi, N., Kang, J. (2010). Image Matching Based on Representative Local Descriptors. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_32

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  • DOI: https://doi.org/10.1007/978-3-642-11301-7_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11300-0

  • Online ISBN: 978-3-642-11301-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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