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Proceedings Paper

Statistical approach for supervised codeword selection
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Paper 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.

Paper Details

Date Published: 8 February 2015
PDF: 7 pages
Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 940609 (8 February 2015); doi: 10.1117/12.2078771
Show Author Affiliations
Kihong Park, Yonsei Univ. (Korea, Republic of)
Seungchul Ryu, Yonsei Univ. (Korea, Republic of)
Seungryong Kim, Yonsei Univ. (Korea, Republic of)
Kwanghoon Sohn, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 9406:
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

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