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Improving shape retrieval by learning graph transduction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 5305 LNCS, Issue: PART 4, Page: 788-801
2008
  • 122
    Citations
  • 0
    Usage
  • 56
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    122
    • Citation Indexes
      122
  • Captures
    56

Conference Paper Description

Shape retrieval/matching is a very important topic in computer vision. The recent progress in this domain has been mostly driven by designing smart features 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 matching algorithms. It learns a better metric through graph transduction by propagating the model through existing shapes, in a way similar to computing geodesics in shape manifold. However, the proposed method does not require learning the shape manifold explicitly and it does not require knowing any class labels of existing shapes. The presented experimental results demonstrate that the proposed approach yields significant improvements over the state-of-art shape matching algorithms. We obtained a retrieval rate of 91% on the MPEG-7 data set, which is the highest ever reported in the literature. © 2008 Springer Berlin Heidelberg.

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