Patch-Level Spatial Layout for Classification and Weakly Supervised Localization
Valentina Zadrija, Josip Krapac, Jakob J. Verbeek, Siniša Šegvić · Lecture notes in computer science · 2015
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. These 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.