Statistical approach for supervised codeword selection

Kihong Park, Seungchul Ryu, Seungryong Kim, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bag-of-words (BoW) is one of the most successful methods for object categorization. This paper proposes a statistical codeword selection algorithm where the best subset is selected from the initial codewords based on the statistical characteristics of codewords. For this purpose, we defined two types of codeword-confidences: cross- and within-category confidences. The cross- and within-category confidences eliminate indistinctive codewords across categories and inconsistent codewords within each category, respectively. An informative subset of codewords is then selected based on these two codeword-confidences. The experimental evaluation for a scene categorization dataset and a Caltech-101 dataset shows that the proposed method improves the categorization performance up to 10% in terms of error rate reduction when cooperated with BoW, sparse coding (SC), and locality-constrained liner coding (LLC). Furthermore, the codeword size is reduced by 50% leading a low computational complexity.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXXII
Subtitle of host publicationAlgorithms and Techniques
EditorsJuha Roning, David Casasent
PublisherSPIE
ISBN (Electronic)9781628414967
DOIs
Publication statusPublished - 2015
EventIntelligent Robots and Computer Vision XXXII: Algorithms and Techniques - San Francisco, United States
Duration: 2015 Feb 92015 Feb 10

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9406
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherIntelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Country/TerritoryUnited States
CitySan Francisco
Period15/2/915/2/10

Bibliographical note

Publisher Copyright:
© 2015 SPIE-IS&T.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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