Spatial histograms of soft pairwise similar patches to improve the bag-of-visual-words model

Abstract : In the context of category level scene classification, the bag-of-visual-words model (BoVW) is widely used for image representation. This model is appearance based and does not contain any information regarding the arrangement of the visual words in the 2D image space. To overcome this problem, recent approaches try to capture information about either the absolute or the relative spatial location of visual words. In the first category, the so-called Spatial Pyramid Representation (SPR) is very popular thanks to its simplicity and good results. Alternatively, adding information about occurrences of relative spatial configurations of visual words was proven to be effective but at the cost of higher computational complexity, specifically when relative distance and angles are taken into account. In this paper, we introduce a novel way to incorporate both distance and angle information in the BoVW representation. The novelty is first to provide a computationally efficient representation adding relative spatial information between visual words and second to use a soft pairwise voting scheme based on the distance in the descriptor space. Experiments on challenging data sets MSRC-2, 15Scene, Caltech101, Caltech256 and Pascal VOC 2007 demonstrate that our method outperforms or is competitive with concurrent ones. We also show that it provides important complementary information to the spatial pyramid matching and can improve the overall performance.
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Rahat Khan, Cecile Barat, Damien Muselet, Christophe Ducottet. Spatial histograms of soft pairwise similar patches to improve the bag-of-visual-words model. Computer Vision and Image Understanding, Elsevier, 2014, 132, pp.102-112. ⟨10.1016/j.cviu.2014.09.005⟩. ⟨ujm-01077476⟩

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