Discriminative Spatial Codebook Generation for Image Classification

Codebook plays an important role in the bag-of-visual-words (BoW) model for image classification. However, the traditional codebook generation procedure ignores the spatial information. Although a lot of works have been done to consider the spatial information for codebook generation, most of them rely on fixed region selection or partition of images, hence are not able to cope with the variations of images. To solve this problem, in this paper, we propose a novel discriminative spatial coding algorithm which can automatically generate and select the most representative codebooks for image representation. This is achieved by first generate a number of spatial codebooks through over-complete image partition with overlap. Second, for each local feature to be encoded, the most discriminative codebook is selected by jointly minimizing the encoding error and the codebook´s spatial distance. Experimental results on several public image datasets show the effectiveness of the proposed discriminative spatial coding method for efficient image classification.