Discriminative Spatial Tree for Image Classification

Spatial pyramid is a very popular method for preserving the spatial information of local features, which partitions an image into multiple blocks at different resolution levels. Nevertheless, the strategy for partitioning an image is designed by hand and same for all the codewords in a codebook. To address this problem, we propose a novel partition method named discriminative spatial tree (ST), and implement it from two different viewpoints: class and image. Discriminative ST builds one spatial tree for each codeword, and each node in each tree corresponds to one image region. For better performance evaluation, we also introduce a simplified coding scheme. Experimental results on two challenging datasets show that our simplified coding scheme leads to comparable results to some sophisticated ones, and discriminative ST can achieve better classification performance than spatial pyramid.