Youssef Hadi

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In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is based on the <i>k-medoid</i> clustering algorithms to find the best representative frame for each video shot. This algorithm, which is applicable to all types of descriptors, consists of extracting key frames by similarity(More)
— In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is based on the k partition algorithms. We choose the ones based on k-medoid clustering methods so as to find the best representative object for each partitions. In order to find the number of partition (i.e. the number of(More)
distribution de ce contenu visuel représente des variables linguistiques, c.-à-d. "l'occurrence est maximale" et "la distribution est maximale". L'occurrence et la distribution représentent deux ensembles flous qui nécessitent une détermination de leur fonction d'appartenance. Dans notre proposition, nous segmentons les séquences vidéo en plans (shots)(More)