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Learning-Based Bipartite Graph Matching for View-Based 3D Model Retrieval | IEEE Journals & Magazine | IEEE Xplore

Learning-Based Bipartite Graph Matching for View-Based 3D Model Retrieval


Abstract:

Distance measure between two sets of views is one central task in view-based 3D model retrieval. In this paper, we introduce a distance metric learning method for biparti...Show More

Abstract:

Distance measure between two sets of views is one central task in view-based 3D model retrieval. In this paper, we introduce a distance metric learning method for bipartite graph matching-based 3D object retrieval framework. In this method, the relationship among 3D models is formulated by a graph structure with semisupervised learning to estimate the model relevance. More specially, we model two sets of views by using a bipartite graph, on which their optimal matching is estimated. Then, we learn a refined distance metric by using the user’s relevance feedback. The proposed method has been evaluated on four data sets and the experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.
Published in: IEEE Transactions on Image Processing ( Volume: 23, Issue: 10, October 2014)
Page(s): 4553 - 4563
Date of Publication: 25 July 2014

ISSN Information:

PubMed ID: 25073171

Funding Agency:


References

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