Learning context-sensitive similarity by shortest path propagation

Authors:
Jingyan Wang;Yongping Li;Xiang Bai;Ying Zhang;Chao Wang;Ning Tang
Affiliations:
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, 2019 Jialuo Road, Shanghai 201800, PR China;Shanghai Institute of Applied Physics, Chinese Academy of Sciences, 2019 Jialuo Road, Shanghai 201800, PR China;Department of Electronics and Information Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei Province 430074, PR China;Shanghai Institute of Applied Physics, Chinese Academy of Sciences, 2019 Jialuo Road, Shanghai 201800, PR China;OGI School of Science and Engineering, Oregon Health & Science University (OHSU), Beaverton, OR 97006, US;Shanghai Institute of Applied Physics, Chinese Academy of Sciences, 2019 Jialuo Road, Shanghai 201800, PR China
Venue:
Pattern Recognition
Year:
2011

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Abstract

In this paper, we introduce a novel shape/object retrieval algorithm shortest path propagation (SSP). Given a query object q and a target database object p, we explicitly find the shortest path between them in the distance manifold of the database objects. Then a new distance measure between q and p is learned based on the database objects on the shortest path to replace the original distance measure. The promising results on both MEPG-7 shape dataset and a protein dataset demonstrate that our method can significantly improve the ranking of the object retrieval.