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Conference Papers Year : 2013

Learning Graphs to Match

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Abstract

Many tasks in computer vision are formulated as graph matching problems. Despite the NP-hard nature of the problem, fast and accurate approximations have led to significant progress in a wide range of applications. Learning graph models from observed data, however, still remains a challenging issue. This paper presents an effective scheme to parameterize a graph model, and learn its structural attributes for visual object matching. For this, we propose a graph representation with histogram-based attributes, and optimize them to increase the matching accuracy. Experimental evaluations on synthetic and real image datasets demonstrate the effectiveness of our approach, and show significant improvement in matching accuracy over graphs with pre-defined structures.
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Dates and versions

hal-00875105 , version 1 (21-10-2013)

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Minsu Cho, Karteek Alahari, Jean Ponce. Learning Graphs to Match. ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. pp.25-32, ⟨10.1109/ICCV.2013.11⟩. ⟨hal-00875105⟩
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