Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Learning Optimal Compact Codebook for Efficient Object Categorization
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
Object Categorization Based on Kernel Principal Component Analysis of Visual Words
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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The Bag-of-Words (BoW) derived from local keypoints was widely applied in visual information research such as image search, video retrieval, object categorization, and computer vision. Construction of visual codebook is a well-known and predominant method for the representation of BoW. However, a visual codebook usually has a high dimension that results in high computational complexity. In this paper, an approach is presented for constructing a compact visual codebook. Two important parameters, namely the likelihood ratio and the significant level, are proposed to estimate the discriminative capability of each of the codewords. Thus, the codewords that have higher discriminative capability are reserved, and the others are removed. Experiments prove that application of the proposed compact codebook not only reduces computational complexity, but also improves performance of object classification.