Towards optimal bag-of-features for object categorization and semantic video retrieval

  • Authors:

  • Yu-Gang Jiang;Chong-Wah Ngo;Jun Yang

  • Affiliations:

  • City University of Hong Kong, Kowloon, Hong Kong;City University of Hong Kong, Kowloon, Hong Kong;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Proceedings of the 6th ACM international conference on Image and video retrieval
  • Year:
  • 2007

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Abstract

Bag-of-features (BoF) deriving from local keypoints has recently appeared promising for object and scene classification. Whether BoF can naturally survive the challenges such as reliability and scalability of visual classification, nevertheless, remains uncertain due to various implementation choices. In this paper, we evaluate various factors which govern the performance of BoF. The factors include the choices of detector, kernel, vocabulary size and weighting scheme. We offer some practical insights in how to optimize the performance by choosing good keypoint detector and kernel. For the weighting scheme, we propose a novel soft-weighting method to assess the significance of a visual word to an image. We experimentally show that the proposed soft-weighting scheme can consistently offer better performance than other popular weighting methods. On both PASCAL-2005 and TRECVID-2006 datasets, our BoF setting generates competitive performance compared to the state-of-the-art techniques. We also show that the BoF is highly complementary to global features. By incorporating the BoF with color and texture features, an improvement of 50% is reported on TRECVID-2006 dataset.