Abstract
The bag-of-words model is one of the most popular representation methods for object categorization. The key idea is to quantize each extracted key point into one of visual words, and then represent each image by a histogram of the visual words. For this purpose, a clustering algorithm (e.g., K-means), is generally used for generating the visual words. Although a number of studies have shown encouraging results of the bag-of-words representation for object categorization, theoretical studies on properties of the bag-of-words model is almost untouched, possibly due to the difficulty introduced by using a heuristic clustering process. In this paper, we present a statistical framework which generalizes the bag-of-words representation. In this framework, the visual words are generated by a statistical process rather than using a clustering algorithm, while the empirical performance is competitive to clustering-based method. A theoretical analysis based on statistical consistency is presented for the proposed framework. Moreover, based on the framework we developed two algorithms which do not rely on clustering, while achieving competitive performance in object categorization when compared to clustering-based bag-of-words representations.
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Acknowledgments
We want to thank the reviewers for helpful comments and suggestions. This research is partially supported by the National Fundamental Research Program of China (2010CB327903), the Jiangsu 333 High-Level Talent Cultivation Program and the National Science Foundation (IIS-0643494). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.
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Zhang, Y., Jin, R. & Zhou, ZH. Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. & Cyber. 1, 43–52 (2010). https://doi.org/10.1007/s13042-010-0001-0
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DOI: https://doi.org/10.1007/s13042-010-0001-0