Fast kernel learning for spatial pyramid matching
Spatial pyramid matching (SPM) is a simple yet effective approach to compute similarity between images. Similarity kernels at different regions and scales are usually fused by some heuristic weights. In this paper, we develop a novel and fast approach to improve SPM by finding the optimal kernel fusing weights from multiple scales, locations, as well as codebooks. One unique contribution of our approach is the novel formulation of kernel matrix learning problem leading to an efficient quadratic programming solution, with much lower complexity than those associated with existing solutions (e.g., semidefinite programming). We demonstrate performance gains of the proposed methods by evaluations over well-known public data sets such as natural scenes and TRECVID 2007.