%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/09.04.17.48
%2 sid.inpe.br/sibgrapi/2017/09.04.17.48.38
%T Representing Indoor Scenes as a Sparse Composition of Feature Segments
%D 2017
%A Silva, Camila Laranjeira da,
%A Nascimento, Erickson Rangel,
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Universidade Federal de Minas Gerais
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ, Brazil
%8 17-20 Oct. 2017
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K Indoor Scene Recognition, Semantic Segmentation, Regularization.
%X Researchers in the fields of Computer Vision and Pattern Recognition have been trying to tackle the problem of scene recognition for many years. Several approaches rely on the assumption that object-level information can be highly discriminatory, which has been extensively validated in the literature. We propose an approach that merges sparse semantic segmentation features with object features, composing a sparse representation of feature segments, as an attempt to represent the composition of objects of a given scene. Our premise is that by adding sparsity constraints to a semantic segmentation feature, we represent a small amount of well chosen objects or parts of objects. We expect this will add robustness to the final feature, since it will recognize a given scene by its most distinctive segments, thus increasing the generalization power of the representation. According to our results, the methodology seems promising, but it is strongly affected by the poor performance of segmentation features on classes containing small objects.
%@language en
%3 Sibgrapi_2017_WiP_camera-ready.pdf