Audiovisual Fusion with Segment Models for Video Structure Analysis

Hidden Markov Models provide a powerful framework for bridging the semantic gap between low-level video features and high-level user needs by taking full advantage of our prior knowledge on the video structure. A serious flaw of HMMs is that they require all the modalities of a video document to be strictly synchronous before their fusion. Taking as a case study tennis broadcasts analysis, we introduce video indexing using Segment Models, a generalization of Hidden Markov Models, where the fusion of different modalities can be performed in a more flexible way. Operating essentially as a layered topology they allow the fusion of asynchronous modalities but do not rely on synchronization points fixed a priori. They also facilitate the fusion of audio models of high-level semantics, like the content of a complete scene, on top of the raw lowlevel audio frames. Segment Models provide encouraging experimental results.