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...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of CVPR 2008|
|01_He_Fast_Kernel_learning_for_2008.pdf||650.72 kB||Adobe PDF||Request a copy|
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