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8 February 2015 Statistical approach for supervised codeword selection
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Proceedings Volume 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques; 940609 (2015) https://doi.org/10.1117/12.2078771
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
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.
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Kihong Park, Seungchul Ryu, Seungryong Kim, and Kwanghoon Sohn "Statistical approach for supervised codeword selection", Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 940609 (8 February 2015); https://doi.org/10.1117/12.2078771
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