Semantic-Spatial Matching for image classification
Spatial Pyramid Matching (SPM) has been proven a simple but effective extension to bag-of-visual-words image representation for spatial layout information compensation. SPM describes image in coarse-to-fine scale by partitioning the image into blocks over multiple levels and the features extracted from each block are concatenated into a long vector representation. Based on the assumption that images from the same class have similar spatial configurations, SPM matches the blocks from different images according to their spatial layout, by aligning all blocks from an image in a fixed spatial order. However, target objects may appear at any location in the image with various backgrounds. Therefore, the fixed spatial matching in SPM fails to match similar objects located different locations. To solve this problem, we propose an effective and efficient block matching method, Semantic-Spatial Matching (SSM). In this method, not only the spatial layout but also the semantic content is considered for block matching. The experiments on two benchmark image classification datasets demonstrate the effectiveness of SSM.