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Capturing video structure with mixture of probabilistic index maps

Abstract : The ability to segment or separate foreground from background in video images is useful to a number of applications including video compression, human-computer interaction, and object tracking to name a few. In order to generate such segmentation in both a reliable and visually pleasing manner the fusion of both spatial and temporal information is required. This fusion typically requires to process a large amount of information thereby imposing a heavy computational cost and/or requiring substantial manual interaction. This heavy computational cost unfortunately limits its applicability. In this paper a generative model to solve this problem is proposed. The model has been designed with a particular emphasis on efficiency, but also provide visually pleasing results. The approach selects salient appearance poses of the foreground shared across the entire sequence in an unsupervised way, and uses them to better extract the foreground from the single frames. Results prove the validity of the approach.
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Submitted on : Sunday, October 5, 2008 - 12:26:49 PM
Last modification on : Monday, March 21, 2022 - 5:22:04 PM
Long-term archiving on: : Monday, October 8, 2012 - 1:56:19 PM


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  • HAL Id : inria-00326713, version 1



A. Perina, M. Cristani, Vittorio Murino, N. Jojic. Capturing video structure with mixture of probabilistic index maps. The 1st International Workshop on Machine Learning for Vision-based Motion Analysis - MLVMA'08, Oct 2008, Marseille, France. ⟨inria-00326713⟩



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