Title :
Audiovisual fusion with segment models for video structure analysis
Author :
Delakis, M. ; Gravier, G. ; Gros, P.
Author_Institution :
IRISA, Univ. of Rennes 1, Rennes, France
fDate :
Nov. 30 2005-Dec. 1 2005
Abstract :
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.
Keywords :
audio-visual systems; hidden Markov models; image segmentation; indexing; sensor fusion; video signal processing; audiovisual fusion; hidden Markov models; segment models; tennis broadcasts analysis; video indexing; video structure analysis;
Conference_Titel :
Integration of Knowledge, Semantics and Digital Media Technology, 2005. EWIMT 2005. The 2nd European Workshop on the (Ref. No. 2005/11099)
Conference_Location :
London
Print_ISBN :
0-86341-595-4