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Conference papers

Accurate Object Localization with Shape Masks

Marcin Marszałek 1 Cordelia Schmid 1 
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : This paper proposes an object class localization approach which goes beyond bounding boxes, as it also determines the outline of the object. Unlike most current localization methods, our approach does not require any hypothesis parameter space to be defined. Instead, it directly generates, evaluates and clusters shape masks. Thus, the presented framework produces much richer answers to the object class localization problem. For example, it easily learns and detects possible object viewpoints and articulations, which are often well characterized by the object outline. We evaluate the proposed approach on the challenging natural-scene Graz-02 object classes dataset. The results demonstrate the extended localization capabilities of our method.
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Submitted on : Monday, December 20, 2010 - 10:28:09 AM
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Marcin Marszałek, Cordelia Schmid. Accurate Object Localization with Shape Masks. CVPR - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2007, Minneapolis, United States. pp.1-8, ⟨10.1109/CVPR.2007.383085⟩. ⟨inria-00548679⟩



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