Toward Plot De-Interlacing in TV Series using Scenes Clustering
Résumé
Multiple sub-stories usually coexist in every episode of a TV series. We propose several variants of an approach for plot de-interlacing based on scenes clustering − with the ultimate goal of providing the end-user with tools for fast and easy overview of one episode, one season or the whole TV series. Each scene can be described in three different ways (based on color histograms, speaker diarization or automatic speech recognition outputs) and four clustering approaches are investigated, one of them based on a graphical representation of the video. Experiments are performed on two TV series of different lengths and formats. We show that semantic descriptors (such as speaker diarization) give the best results and underline that our approach provides useful information for plot de-interlacing.