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

Content Provider	IEEE Xplore Digital Library
Author	Ke Lu Rongrong Ji Jinhui Tang Yue Gao
Copyright Year	1992
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.
Sponsorship	IEEE Signal Processing Society
Starting Page	4553
Ending Page	4563
Page Count	11
File Size	4204929
File Format	PDF
ISSN	10577149
Volume Number	23
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Solid modeling Three-dimensional displays Bipartite graph Measurement Feature extraction Computational modeling Semisupervised learning metric learning 3D model retrieval view-based bipartite matching
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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