Toward a discriminative codebook: Codeword selection across multi-resolution

In patch-based object recognition, there are two important issues on the codebook generation: (1) resolution: a coarse codebook lacks sufficient discriminative power, and an over-fine one is sensitive to noise; (2) codeword selection: non-discriminative codewords not only increase the codebook size, but also can hurt the recognition performance. To achieve a discriminative codebook for better recognition, this paper argues that these two issues are strongly related and should be solved as a whole. In this paper, a multi-resolution codebook is first designed via hierarchical clustering. With a reasonable size, it includes all of the codewords which cross a large number of resolution levels. More importantly, it forms a diverse candidate codeword set that is critical to codeword selection. A Boosting feature selection approach is modified to select the discriminative codewords from this multi-resolution codebook. By doing so, the obtained codebook is composed of the most discriminative codewords culled from different levels of resolution. Experimental study demonstrates the better recognition performance attained by this codebook.