Issue No. 05 - May (2010 vol. 32)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.85
Xiang Bai , Huazhong University of Science and Technology, Wuhan
Xingwei Yang , Temple University, Philadelphia
Longin Jan Latecki , Temple University, Philadelphia
Wenyu Liu , Huazhong University of Science and Technology, Wuhan
Zhuowen Tu , University of California, Los Angeles, Los Angeles
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 over the state-of-art shape matching algorithms. We obtained a retrieval rate of 91.61 percent on the MPEG-7 data set, which is the highest ever reported in the literature. Moreover, the learned similarity by the proposed method also achieves promising improvements on both shape classification and shape clustering.
Shape similarity, shape retrieval, shape classification, shape clustering, graph transduction.
X. Yang, Z. Tu, X. Bai, L. J. Latecki and W. Liu, "Learning Context-Sensitive Shape Similarity by Graph Transduction," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 861-874, 2009.