@inproceedings{f5064d1b50984e9e809ce82eb901a49c,
title = "Statistical approach for supervised codeword selection",
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.",
author = "Kihong Park and Seungchul Ryu and Seungryong Kim and Kwanghoon Sohn",
year = "2015",
month = jan,
day = "1",
doi = "10.1117/12.2078771",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Juha Roning and David Casasent",
booktitle = "Proceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXXII",
address = "United States",
note = "Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques ; Conference date: 09-02-2015 Through 10-02-2015",
}