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A probabilistic framework of selecting effective key frames for video browsing and indexing

Riad Hammoud 1 Roger Mohr 1 
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : To represent effectively the video content, for browsing, indexing and video skimming, the most characteristic frames (called key-frames) should be extracted from given shots. This paper, briefly reviews and evaluates the existing approaches of key-frames extraction; and then introduces a framework of selecting effective key-frames using an unsupervised clustering method. The mixture of Gaussians is used to model the temporal variation of the feature vectors of all frames in the shot. As a result, the feature-based representation of the shot is partitioned into several clusters. From each obtained cluster, firstly the closest frame to the median of its frames is selected as a reference key-frame. Then depending on the variation in time and appearance of the cluster content against the reference key-frame multiple frames can be extracted to represent effectively the cluster. The number of clusters is determined automatically by the Bayes Information Criterion. Experimental results on tracked objects in a real-world video stream are presented which illustrate the performance of the proposed technique.
keyword : MOVI
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Submitted on : Tuesday, December 21, 2010 - 5:03:20 PM
Last modification on : Friday, February 4, 2022 - 3:19:48 AM
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  • HAL Id : inria-00548295, version 1



Riad Hammoud, Roger Mohr. A probabilistic framework of selecting effective key frames for video browsing and indexing. International workshop on Real-Time Image Sequence Analysis (RISA '00), Aug 2000, Oulu, Finland. pp.79--88. ⟨inria-00548295⟩



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