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A cortical framework for scene categorization
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Orientador(es)
Resumo(s)
Human observers can very rapidly and accurately categorise scenes. This is context or gist vision. In this paper
we present a biologically plausible scheme for gist vision which can be integrated into a complete cortical vision architecture. The model is strictly bottom-up, employing state-of-the-art models for feature extractions.
It combines five cortical feature sets: multiscale lines and edges and their dominant orientations, the density of
multiscale keypoints, the number of consistent multiscale regions, dominant colours in the double-opponent colour channels, and significant saliency in covert attention regions. These feature sets are processed in a hierarchical set of layers with grouping cells, which serve to characterise five image regions: left, right, top, bottom and centre. Final scene classification is obtained by a trained decision tree.
Descrição
Palavras-chave
Visão humana
Contexto Educativo
Citação
Rodrigues, J.M.F.; du Buf, J.M.H. A cortical framework for scene categorization, Trabalho apresentado em VISAPP 2011, In Proc. Int. Conf. on Computer Vision Theory and Applications (VISAPP 2011), Vilamoura, Portugal, 2011.