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Erschienen in: International Journal of Multimedia Information Retrieval 1/2015

01.03.2015 | Regular Paper

VIDCAR: an unsupervised CBVR framework for identifying similar videos with prominent object motion

verfasst von: Chiranjoy Chattopadhyay

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 1/2015

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Abstract

This paper presents VIDeo Content Analysis and Retrieval (VIDCAR), an unsupervised framework for Content-Based Video Retrieval (CBVR) using representation of the dynamics in the spatio-temporal model extracted from video shots. We propose Dynamic Multi Spectro Temporal-Curvature Scale Space (DMST-CSS), an improved feature descriptor for enhancing the performance of CBVR task. Our primary contribution is in representation of the dynamics of the evolution of the MST-CSS surface. Unlike the earlier MST-CSS descriptor [22], which extracts geometric features after the evolving MST-CSS surface converges to a final formation, this DMST-CSS captures the dynamics of the evolution (formation) of the surface and is thus more robust. We have represented the dynamics of MST-CSS surface as a multivariate time series to obtain a DMST-CSS descriptor. A global kernel alignment technique has been adapted to compute a match cost between query and model DMST-CSS descriptor. In our experiments, VIDCAR was shown to have greater precision recall than the competitors on five datasets.

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Metadaten
Titel
VIDCAR: an unsupervised CBVR framework for identifying similar videos with prominent object motion
verfasst von
Chiranjoy Chattopadhyay
Publikationsdatum
01.03.2015
Verlag
Springer London
Erschienen in
International Journal of Multimedia Information Retrieval / Ausgabe 1/2015
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-014-0070-z

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