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View-based 3D object retrieval by bipartite graph matching

Authors:

Rongrong JiAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 897 - 900

https://doi.org/10.1145/2393347.2396341

Published: 29 October 2012 Publication History

Abstract

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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Cited By

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https://doi.org/10.1007/s11042-019-08043-9
Xie JZhu FDai GShao LFang Y(2017)Progressive Shape-Distribution-Encoder for Learning 3D Shape RepresentationIEEE Transactions on Image Processing10.1109/TIP.2017.265140826:3(1231-1242)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2651408
Huang YChen TWang HQiu WFang Z(2017)Fast quadratic-programming-based graph matching algorithm with image applicationsMultimedia Tools and Applications10.1007/s11042-017-5226-4Online publication date: 12-Oct-2017
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Index Terms

View-based 3D object retrieval by bipartite graph matching
1. Applied computing

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Copyright © 2012 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Citations

Cited By

Ji PLiu X(2019)A fast and efficient 3D reflection symmetry detector based on neural networksMultimedia Tools and Applications10.1007/s11042-019-08043-9Online publication date: 21-Aug-2019
https://doi.org/10.1007/s11042-019-08043-9
Xie JZhu FDai GShao LFang Y(2017)Progressive Shape-Distribution-Encoder for Learning 3D Shape RepresentationIEEE Transactions on Image Processing10.1109/TIP.2017.265140826:3(1231-1242)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2651408
Huang YChen TWang HQiu WFang Z(2017)Fast quadratic-programming-based graph matching algorithm with image applicationsMultimedia Tools and Applications10.1007/s11042-017-5226-4Online publication date: 12-Oct-2017
https://doi.org/10.1007/s11042-017-5226-4
Leng BGuo SDu CZeng JXiong Z(2017)3D Object retrieval based on viewpoint segmentationMultimedia Systems10.1007/s00530-015-0454-923:1(19-28)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00530-015-0454-9
Fu-Chang LShuang-Jian WJin-Rong WZhi-Geng PChen Y(2016)3D Model Retrieval Based on Fuzzy Correspondences and Hybrid Shape Features2016 International Conference on Virtual Reality and Visualization (ICVRV)10.1109/ICVRV.2016.67(358-363)Online publication date: Sep-2016
https://doi.org/10.1109/ICVRV.2016.67
Xiao JFeng YJi MZhuang Y(2016)Fast view-based 3D model retrieval via unsupervised multiple feature fusion and online projection learningSignal Processing10.1016/j.sigpro.2014.11.020120:C(702-713)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.sigpro.2014.11.020
Ji MFeng YXiao JZhuang YYang XZhang J(2015)Efficient semi-supervised multiple feature fusion with out-of-sample extension for 3D model retrievalNeurocomputing10.1016/j.neucom.2014.12.112169(23-33)Online publication date: Dec-2015
https://doi.org/10.1016/j.neucom.2014.12.112
Liu AXu NSu YLin HHao TYang Z(2015)Single/multi-view human action recognition via regularized multi-task learningNeurocomputing10.1016/j.neucom.2014.04.090151(544-553)Online publication date: Mar-2015
https://doi.org/10.1016/j.neucom.2014.04.090
Savelonas MPratikakis ISfikas K(2015)An overview of partial 3D object retrieval methodologiesMultimedia Tools and Applications10.1007/s11042-014-2267-974:24(11783-11808)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s11042-014-2267-9
Leng BZhang XYao MXiong Z(2014)3D Object Classification Using Deep Belief NetworksProceedings of the 20th Anniversary International Conference on MultiMedia Modeling - Volume 832610.1007/978-3-319-04117-9_12(128-139)Online publication date: 6-Jan-2014
https://dl.acm.org/doi/10.1007/978-3-319-04117-9_12

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