Keypoints and codewords selection for efficient bag-of-features representation

Abstract

Bag-of-features (BoF) representation is one of the most popular image representations that is used in visual object classification, owing to its simplicity and good performance. However, the BoF representation always faces the difficulty of curse of dimensionality that leads to huge computational cost and increased storage requirement. To create a discriminative and compact BoF representation, it is desired to eliminate ambiguous features before the construction of visual codebook and to select the informative codewords from the constructed codebook. In this paper, we propose a two-staged approach to create a discriminative and compact BoF representation for object recognition. In the first step, we eliminate ambiguous patch-based descriptors using an entropy-based filtering approach to retain high-quality descriptors. In the subsequent step, we select the informative codewords based on statistical measures. We have tested the proposed technique on Xerox7, UIUC texture, PASCAL VOC 2007 and Caltech101 benchmark datasets. Testing results show that more training features and/or a high-dimensional codebook do not contribute significantly to increase the performance of classification but it increases the overall model complexity and computational cost. The proposed preprocessing step of descriptor selection increases the discriminative power of a codebook, whereas the post-processing step of codeword selection maintains the codebook to be more compact. The proposed framework would help to optimise BoF representation to be efficient with steady performance.