View-based 3D Object Retrieval by Bipartite Graph Matching
Bipartite graph matching has been investigated in multiple view matching for 3D object retrieval. However, existing methods employ one-to-one vertex matching scheme while more than two views may share close semantic meanings in practice. In this work, we propose a bipartite graph matching method to...
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sg-smu-ink.sis_research-26472018-07-13T03:09:30Z View-based 3D Object Retrieval by Bipartite Graph Matching WEN, Yue GAO, Yue HONG, Richang LUAN, Huanbo LIU, Qiong SHEN, Jialie JI, Rongrong Bipartite graph matching has been investigated in multiple view matching for 3D object retrieval. However, existing methods employ one-to-one vertex matching scheme while more than two views may share close semantic meanings in practice. In this work, we propose a bipartite graph matching method to measure the distance between two objects based on multiple views. In the proposed method, representative views are first selected by using view clustering for each object, and the corresponding weights are given based on the cluster results. A bipartite graph is constructed by using the two groups of representative views from two compared objects. To calculate the similarity between two objects, the bipartite graph is first partitioned to several subsets, and the views in the same sub-set are with high possibility to be with similar semantic meanings. The distances between two objects within individual subsets are then assembled through the graph to obtain the final similarity. Experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed algorithm. 2012-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1648 info:doi/10.1145/2393347.2396341 http://dx.doi.org/10.1145/2393347.2396341 http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University 3D object retrieval bipartite graph graph matching Databases and Information Systems |
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3D object retrieval bipartite graph graph matching Databases and Information Systems WEN, Yue GAO, Yue HONG, Richang LUAN, Huanbo LIU, Qiong SHEN, Jialie JI, Rongrong View-based 3D Object Retrieval by Bipartite Graph Matching |
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Bipartite graph matching has been investigated in multiple view matching for 3D object retrieval. However, existing methods employ one-to-one vertex matching scheme while more than two views may share close semantic meanings in practice. In this work, we propose a bipartite graph matching method to measure the distance between two objects based on multiple views. In the proposed method, representative views are first selected by using view clustering for each object, and the corresponding weights are given based on the cluster results. A bipartite graph is constructed by using the two groups of representative views from two compared objects. To calculate the similarity between two objects, the bipartite graph is first partitioned to several subsets, and the views in the same sub-set are with high possibility to be with similar semantic meanings. The distances between two objects within individual subsets are then assembled through the graph to obtain the final similarity. Experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed algorithm. |
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WEN, Yue GAO, Yue HONG, Richang LUAN, Huanbo LIU, Qiong SHEN, Jialie JI, Rongrong |
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WEN, Yue GAO, Yue HONG, Richang LUAN, Huanbo LIU, Qiong SHEN, Jialie JI, Rongrong |
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WEN, Yue |
title |
View-based 3D Object Retrieval by Bipartite Graph Matching |
title_short |
View-based 3D Object Retrieval by Bipartite Graph Matching |
title_full |
View-based 3D Object Retrieval by Bipartite Graph Matching |
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View-based 3D Object Retrieval by Bipartite Graph Matching |
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View-based 3D Object Retrieval by Bipartite Graph Matching |
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view-based 3d object retrieval by bipartite graph matching |
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Institutional Knowledge at Singapore Management University |
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2012 |
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https://ink.library.smu.edu.sg/sis_research/1648 http://dx.doi.org/10.1145/2393347.2396341 |
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