Improving scene classification with weakly spatial symmetry information
The bag-of-visual-words (BOW) model has been widely used in the field of scene classification. Since it ignores the spatial information, the spatial-pyramid-matching (SPM) model [1] was presented by partitioning the image into increasingly fine blocks and computing histograms of local features in each block. However, the spatial symmetry has never been considered explicitly in scene classification as we known. In this paper, a novel descriptor named weakly spatial symmetry (WSS) is proposed to boost the performance of image classification. After region segmentation, the spatial symmetry is represented by L1 distances of region histograms. Four kinds of spatial symmetry are extracted in blocks of increasing scales as in SPM [1]. The WSS descriptor can be used independently or combined with BOW or SPM for scene classification. Experiments on scene-15 and caltech 101 dataset demonstrate the effectiveness of the proposed approach.