Article

Beyond pairwise shape similarity analysis

Authors:

Peter Kontschieder,

Michael Donoser,

Horst BischofAuthors Info & Claims

ACCV'09: Proceedings of the 9th Asian conference on Computer Vision - Volume Part III

Pages 655 - 666

https://doi.org/10.1007/978-3-642-12297-2_63

Published: 23 September 2009 Publication History

Abstract

This paper considers two major applications of shape matching algorithms: (a) query-by-example, i e retrieving the most similar shapes from a database and (b) finding clusters of shapes, each represented by a single prototype Our approach goes beyond pairwise shape similarity analysis by considering the underlying structure of the shape manifold, which is estimated from the shape similarity scores between all the shapes within a database We propose a modified mutual kNN graph as the underlying representation and demonstrate its performance for the task of shape retrieval We further describe an efficient, unsupervised clustering method which uses the modified mutual kNN graph for initialization Experimental evaluation proves the applicability of our method, e g by achieving the highest ever reported retrieval score of 93.40% on the well known MPEG-7 database.

References

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Cited By

Valem LOliveira CPedronette DAlmeida J(2018)Unsupervised Similarity Learning through Rank Correlation and kNN SetsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/324105314:4(1-23)Online publication date: 23-Oct-2018
https://dl.acm.org/doi/10.1145/3241053
Bai SBai XTian QLatecki LSingh SMarkovitch S(2017)Regularized diffusion process for visual retrievalProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298023.3298145(3967-3973)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298023.3298145
Valem LPedronette D(2017)Selection and Combination of Unsupervised Learning Methods for Image RetrievalProceedings of the 15th International Workshop on Content-Based Multimedia Indexing10.1145/3095713.3095741(1-6)Online publication date: 19-Jun-2017
https://dl.acm.org/doi/10.1145/3095713.3095741
Show More Cited By

Beyond pairwise shape similarity analysis
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ACCV'09: Proceedings of the 9th Asian conference on Computer Vision - Volume Part III

September 2009

678 pages

ISBN:3642122965

Editors:
Hongbin Zha
Department of Machine Intelligence, Peking University, Beijing, China
,
Rin-ichiro Taniguchi
Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
,
Stephen Maybank
Birkbeck College, Department of Computer Science, University of London, London, UK

Sponsors

NSF of China: National Natural Science Foundation of China
Fujitsu
Microsoft Research: Microsoft Research
Key Laboratory of Machine Perception (MOE), Peking University: Key Laboratory of Machine Perception (MOE), Peking University
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 September 2009

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Cited By

Valem LOliveira CPedronette DAlmeida J(2018)Unsupervised Similarity Learning through Rank Correlation and kNN SetsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/324105314:4(1-23)Online publication date: 23-Oct-2018
https://dl.acm.org/doi/10.1145/3241053
Bai SBai XTian QLatecki LSingh SMarkovitch S(2017)Regularized diffusion process for visual retrievalProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298023.3298145(3967-3973)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298023.3298145
Valem LPedronette D(2017)Selection and Combination of Unsupervised Learning Methods for Image RetrievalProceedings of the 15th International Workshop on Content-Based Multimedia Indexing10.1145/3095713.3095741(1-6)Online publication date: 19-Jun-2017
https://dl.acm.org/doi/10.1145/3095713.3095741
Khalid SSajjad SJabbar SChang H(2017)Accurate and efficient shape matching approach using vocabularies of multi-feature space representationsJournal of Real-Time Image Processing10.1007/s11554-015-0545-z13:3(449-465)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11554-015-0545-z
Liu RWang RYu XAn L(2017)Shape automatic clustering-based multi-objective optimization with decompositionMachine Vision and Applications10.1007/s00138-017-0850-628:5-6(497-508)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s00138-017-0850-6
Zheng DLiu WWang H(2017)Improving Shape Retrieval by Fusing Generalized Mean First-Passage TimeNeural Information Processing10.1007/978-3-319-70093-9_46(439-448)Online publication date: 14-Nov-2017
https://dl.acm.org/doi/10.1007/978-3-319-70093-9_46
Pedronette DTorres RKender JSmith JLuo JBoll SHsu W(2016)Rank Diffusion for Context-Based Image RetrievalProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912060(321-325)Online publication date: 6-Jun-2016
https://dl.acm.org/doi/10.1145/2911996.2912060
Yang CTiebe OShirahama KGrzegorzek M(2016)Object matching with hierarchical skeletonsPattern Recognition10.1016/j.patcog.2016.01.02255:C(183-197)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.patcog.2016.01.022
Pedronette DTorres R(2016)A correlation graph approach for unsupervised manifold learning in image retrieval tasksNeurocomputing10.1016/j.neucom.2016.03.081208:C(66-79)Online publication date: 5-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.03.081
Pedronette DTorres R(2016)Combining re-ranking and rank aggregation methods for image retrievalMultimedia Tools and Applications10.1007/s11042-015-3044-075:15(9121-9144)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11042-015-3044-0
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