Improved Spatial Pyramid Matching for Image Classification

Abstract

AbstractSpatial analysis of salient feature points has been shown to be promising in image analysis and classification. In the past, spatial pyramid matching makes use of both of salient feature points and spatial multiresolution blocks to match between images. However, it is shown that different images or blocks can still have similar features using spatial pyramid matching. The analysis and matching will be more accurate in scale space. In this paper, we propose to do spatial pyramid matching in scale space. Specifically, pyramid match histograms are computed in multiple scales to refine the kernel for support vector machine classification. We show that the combination of salient point features, scale space and spatial pyramid matching improves the original spatial pyramid matching significantly.KeywordsSupport Vector MachineScale SpaceClass AccuracySpatial PyramidCodebook SizeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.