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International Conference on Advanced Concepts for Intelligent Vision Systems

ACIVS 2016: Advanced Concepts for Intelligent Vision Systems pp 398–409Cite as

Vector Quantization Enhancement for Computer Vision Tasks

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10016))

Abstract

This paper augments the Bag-of-Word scheme in several respects: we incorporate a category label into the clustering process, build classifier-tailored codebooks, and weight codewords according to their probability to occur. A size-adaptive feature clustering algorithm is also proposed as an alternative to k-means. Experiments on the PASCAL VOC 2007 challenge validate the approach for classical hard-assignment as well as VLAD encoding.

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Acknowledgement

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289.

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Authors and Affiliations

  1. Insight Centre for Data Analytics, Dublin City University, Glasnevin, Ireland

    Remi Trichet & Noel E. O’Connor

Authors
  1. Remi Trichet
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  2. Noel E. O’Connor
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Corresponding author

Correspondence to Remi Trichet .

Editor information

Editors and Affiliations

  1. Université Paris-Sud 11 , Orsay, France

    Jacques Blanc-Talon

  2. University of Salento , Lecce, Lecce, Italy

    Cosimo Distante

  3. Ghent University , Gent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre , Sydney, New South Wales, Australia

    Dan Popescu

  5. University of Antwerp , Wilrijk, Belgium

    Paul Scheunders

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© 2016 Springer International Publishing AG

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Trichet, R., O’Connor, N.E. (2016). Vector Quantization Enhancement for Computer Vision Tasks. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_35

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  • DOI: https://doi.org/10.1007/978-3-319-48680-2_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48679-6

  • Online ISBN: 978-3-319-48680-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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