A probabilistic framework of selecting effective key frames for video browsing and indexing - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2000

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

Résumé

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.

Mots clés

Fichier principal
Vignette du fichier
hammoud_RISA2000.pdf (734.07 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

inria-00548295 , version 1 (21-12-2010)

Identifiants

  • HAL Id : inria-00548295 , version 1

Citer

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⟩
146 Consultations
63 Téléchargements

Partager

Gmail Facebook X LinkedIn More