Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Beyond shot retrieval: searching for broadcast news items using language models of concepts
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Exploiting result consistency to select query expansions for spoken content retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Exploiting noisy visual concept detection to improve spoken content based video retrieval
Proceedings of the international conference on Multimedia
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In this paper we present a novel approach to semantic-theme-based 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.