Patch-Level Spatial Layout For Classification And Weakly Supervised Localization

We propose a discriminative patch-level model which combines appearance and spatial layout cues. We start from a block-sparse model of patch appearance based on the normalized Fisher vector representation. The appearance model is responsible for (i) selecting a discriminative subset of visual words, and (ii) identifying distinctive patches assigned to the selected subset. These patches are further filtered by a sparse spatial model operating on a novel representation of pairwise patch layout. We have evaluated the proposed pipeline in image classification and weakly supervised localization experiments on a public traffic sign dataset. The results show significant advantage of the combined model over state of the art appearance models.