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2010

Learning Context-Sensitive Shape Similarity by Graph Transduction

8 years 9 months ago
Learning Context-Sensitive Shape Similarity by Graph Transduction
—Shape similarity and shape retrieval are very important topics in computer vision. The recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape in a graph structure. Our method is general and can be built on top of any existing shape similarity measure. For a given similarity measure, a new similarity is learned through graph transduction. The new similarity is learned iteratively so that the neighbors of a given shape influence its final similarity to the query. The basic idea here is related to PageRank ranking, which forms a foundation of Google Web search. The presented experimental results demonstrate that the proposed approach yields significant improvements
Xiang Bai, Xingwei Yang, Longin Jan Latecki, Wenyu
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PAMI
Authors Xiang Bai, Xingwei Yang, Longin Jan Latecki, Wenyu Liu, Zhuowen Tu
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