Exploiting noisy visual concept detection to improve spoken content based video retrieval

Authors:
Stevan Rudinac;Martha Larson;Alan Hanjalic
Affiliations:
Multimedia Information Retrieval Lab, Delft University of Technology, Delft, Netherlands;Multimedia Information Retrieval Lab, Delft University of Technology, Delft, Netherlands;Multimedia Information Retrieval Lab, Delft University of Technology, Delft, Netherlands
Venue:
Proceedings of the international conference on Multimedia
Year:
2010

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Abstract

In this paper, we present a technique for unsupervised construction of concept vectors, concept-based representations of complete video units, from the noisy shot-level output of a set of visual concept detectors. We deploy these vectors to improve spoken-content-based video retrieval using Query Expansion Selection (QES). Our QES approach analyzes results lists returned in response to several alternative query expansions, applying a coherence indicator calculated on top-ranked items to choose the appropriate expansion. The approach is data driven, does not require prior training and relies solely on the analysis of the collection being queried and the results lists produced for the given query text. The experiments, performed on two datasets, TRECVID 2007/2008 and TRECVID 2009, demonstrate the effectiveness of our approach and show that a small set of well-selected visual concept detectors is sufficient to improve retrieval performance.