Learning-Based Bipartite Graph Matching for View-Based 3D Model Retrieval
- 25 July 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 23 (10), 4553-4563
- https://doi.org/10.1109/tip.2014.2343460
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.Keywords
Funding Information
- National Natural Science Foundation of China (61373076, 61271435, U1301251)
- Fundamental Research Funds for the Central Universities (2013121026)
- 985 Project, Xiamen University, Xiamen, China
- Beijing Natural Science Foundation, Beijing, China (4141003)
- Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (IDHT20130225)
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