Discriminative model fusion for semantic concept detection and annotation in video

Abstract

In this paper we describe a general information fusion algorithm that can be used to incorporate multimodal cues 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.