Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2009

Abstract

Query-to-concept mapping plays one of the keys to concept-based video retrieval. Conventional approaches try to find concepts that are likely to co-occur in the relevant shots from the lexical or statistical aspects. However, the high probability of co-occurrence alone cannot ensure its effectiveness to distinguish the relevant shots from the irrelevant ones. In this paper, we propose distribution-based concept selection (DBCS) for query-to-concept mapping by analyzing concept score distributions of within and between relevant and irrelevant sets. In view of the imbalance between relevant and irrelevant examples, two variants of DBCS are proposed respectively by considering the two-sided and onesided metrics of concept distributions. Specifically, the impact of positive and negative concepts toward search is explicitly considered. DBCS is found to be appropriate for both automatic and interactive video search. Using TRECVID 2008 video dataset for experiments, improvements of 50% and 34% are reported when compared to text-based and visual-based query-to-concept mapping respectively in automatic search. Meanwhile, DBCS shows continuous improvement for all rounds of user feedbacks in interactive search.

Keywords

Concept-based video retrieval, Distribution, Query-to-concept mapping

Discipline

Databases and Information Systems | Data Storage Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 17th ACM international conference on Multimedia, MM'09, Beijing, China, October 19-24

First Page

645

Last Page

648

ISBN

9781605586083

Identifier

10.1145/1631272.1631378

Publisher

ACM

City or Country

Beijing, China

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