In this paper we describe a general information fusion algorithm that an be used to in orporate multimodal ues in building user-defined semantic concept models.We compare this technique with a Bayesian Network-based approach on a semantic concept detection task. Results indicate that this technique yields superior performance.We demonstrate this approach further by building classifiers of arbitrary concepts in a score space defined by a pre-deployed set of multimodal concepts.Results show annotation for user-defined concepts both in and outside the pre-deployed set is competitive with our best video-only models on the TREC Video 2002 corpus.
By: Giridharan Iyengar, Harriet J. Nock, Chalapathy Neti
Published in: RC22873 in 2003
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