Distribution-based similarity measures for Multi-dimensional point set retrieval applications

Shao, Jie, Huang, Zi, Shen, Heng Tao, Shen, Jialie and Zhou, Xiaofang (2008). Distribution-based similarity measures for Multi-dimensional point set retrieval applications. In: A. El-Saddik, S. Vuong, C. Griwodz, A. Del Bimbo, K. Selcuk Candan and A. Jaimes, Proceedings of the 16th ACM International Conference on Multimedia. 16th ACM International Conference on Multimedia, Vancouver BC, Canada, (429-438). 27-31 October 2008. doi:10.1145/1459359.1459417

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Author Shao, Jie
Huang, Zi
Shen, Heng Tao
Shen, Jialie
Zhou, Xiaofang
Title of paper Distribution-based similarity measures for Multi-dimensional point set retrieval applications
Conference name 16th ACM International Conference on Multimedia
Conference location Vancouver BC, Canada
Conference dates 27-31 October 2008
Proceedings title Proceedings of the 16th ACM International Conference on Multimedia
Journal name MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops
Place of Publication New York, U.S A.
Publisher Association for Computing Machinery
Publication Year 2008
Sub-type Fully published paper
DOI 10.1145/1459359.1459417
ISBN 9781605583037
Editor A. El-Saddik
S. Vuong
C. Griwodz
A. Del Bimbo
K. Selcuk Candan
A. Jaimes
Start page 429
End page 438
Total pages 10
Collection year 2009
Language eng
Formatted Abstract/Summary
Effective and efficient method of similarity assessment continues to be one of the most fundamental problems in multimedia data analysis. In case of retrieving relevant items from a collection of objects based on series of multivariate observations (e.g., searching the similar video clips in a repository to a query example), satisfactory performance cannot be expected using many conventional similarity measures based on the aggregation of element pairwise comparisons. Some correlation information among the individual elements has also been investigated to characterize each set of multidimensional points for ranked retrieval, by making use of an unwarranted assumption that the underlying data distribution has a particular parametric form. Motivated by this observation, this paper introduces a novel collective gauge of relevance ranking by evaluating the probabilities that point sets are consistent with the same distribution of the query. Two non-parametric hypothesis tests in statistics are justified to exploit the distributional discrepancy of samples for assessing the similarity between two ensembles of points. While our methodology is mainly presented in the context of video similarity search, it enjoys great flexibility and can be easily adapted to other applications involving generic multi-dimensional point set representation for each object such as human gesture recognition.
Copyright 2008 ACM.
Subjects E1
0806 Information Systems
890205 Information Processing Services (incl. Data Entry and Capture)
Keyword Hypothesis tests
Minimal spanning tree
Multi-dimensional point set
Non-parametric
Reproducing kernel hilbert space
Similarity measures
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Fri, 17 Apr 2009, 14:19:38 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering