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HAH-tree: towards a multidimensional index structure supporting different video modelling approaches in a video database management system

HAH-tree: towards a multidimensional index structure supporting different video modelling... This paper proposes a multidimensional distance-based index structure for video data which supports the three important video modelling approaches namely hierarchical unit-based modelling, feature-based modelling and video semantics modelling seamlessly within one single framework. These three modelling techniques collectively capture and contain the important aspects of the users' information need during content-based video retrieval. The index is built based on the low-level features of the video data, and the hierarchical containment relationships as well as the video semantics are introduced into the index space with an efficient data signature and a stochastic model, respectively. Efficient k-NN algorithms are proposed to emulate popular content-based video retrieval approaches in a multidimensional distance-based index structure. Extensive experimental results demonstrate the capability of the index structure to generate relevant query results with low computational overhead. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

HAH-tree: towards a multidimensional index structure supporting different video modelling approaches in a video database management system

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2010.031888
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a multidimensional distance-based index structure for video data which supports the three important video modelling approaches namely hierarchical unit-based modelling, feature-based modelling and video semantics modelling seamlessly within one single framework. These three modelling techniques collectively capture and contain the important aspects of the users' information need during content-based video retrieval. The index is built based on the low-level features of the video data, and the hierarchical containment relationships as well as the video semantics are introduced into the index space with an efficient data signature and a stochastic model, respectively. Efficient k-NN algorithms are proposed to emulate popular content-based video retrieval approaches in a multidimensional distance-based index structure. Extensive experimental results demonstrate the capability of the index structure to generate relevant query results with low computational overhead.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2010

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