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

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 techniqu...

• Type: Conference
• Year: 2007
• Where: CIVR
• Authors: Yu-Gang Jiang, Chong-Wah Ngo, Jun Yang 0003
• Keywords: BoF Setting Generates, CIVR 2007, Keypoint Detector, Weighting Scheme