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Visual Concept-based Selection of Query Expansions for Spoken Content Retrieval by Stevan Rudinac; Martha Larson; Alan Hanjalic is a scholarly article available to read on EtoBox.

What is Visual Concept-based Selection of Query Expansions for Spoken Content Retrieval about?

In this paper we present a novel approach to semantic-themebased video retrieval that considers entire videos as retrieval units and exploits automatically detected visual concepts to improve the results of retrieval based on spoken content. We deploy a query prediction method that makes use of a coherence indicator calculated on top returned documents and taking into account the information about visual concepts presence in videos to make a choice between query expansion methods. The main contribution of our approach is in its ability to exploit noisy shot-level concept detection to improve semantic-theme-based video retrieval. Strikingly, improvement is possible using an extremely limited set of concepts. In the experiments performed on TRECVID 2007 and 2008 datasets our approach shows an interesting performance improvement compared to the best performing baseline.

Author

Stevan Rudinac; Martha Larson; Alan Hanjalic

Publisher

ACM

Published

2010

Language

EN