Common Spatial Pattern Discovery by Efficient Candidate Pruning

Automatically discovering common visual patterns in images is very challenging due to the uncertainties in the visual appearances of such spatial patterns and the enormous computational cost involved in exploring the huge solution space. Instead of performing exhaustive search on all possible candidates of such spatial patterns at various locations and scales, this paper presents a novel and very efficient algorithm for discovering common visual patterns by designing a provably correct and computationally efficient pruning procedure that has a quadratic complexity. This new approach is able to efficiently search a set of images for unknown visual patterns that exhibit large appearance variations because of rotation, scale changes, slight view changes, color variations and partial occlusions.