Video Story Segmentation with Multi-Modal Features: Experiments on TRECvid 2003

Abstract : This paper describes the first steps of CLIPS/IMAG on the TREC video story segmentation task. We mostly describe the multi-modal features used and their respective performance for the story segmentation task. These features are based on the audio, video and text modalities. The preliminary system, which has the advantage to be relatively free with respect to the use of training data, is also presented in this paper. First experiments on the TRECVID 2003 evaluation set lead to a recall rate of 0.613 and a precision rate of 0.467.
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Conference papers
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https://hal.inria.fr/hal-00953929
Contributor : Marie-Christine Fauvet <>
Submitted on : Friday, February 28, 2014 - 4:06:52 PM
Last modification on : Monday, July 8, 2019 - 3:09:47 PM

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  • HAL Id : hal-00953929, version 1

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Laurent Besacier, Georges Quénot, Stéphane Ayache, Daniel Moraru. Video Story Segmentation with Multi-Modal Features: Experiments on TRECvid 2003. 6th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR'04), 2004, New York, NY, United States. ⟨hal-00953929⟩

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