In this paper we propose a novel and complete video structuring/ segmentation framework, which includes shot boundary detection, keyframe selection and high level clustering of shots into scenes. In a first stage, an enhanced shot boundary detection algorithm is proposed. The approach extends the state-of-the-art graph partition model and exploits a scale space filtering of the similarity signal which makes it possible to significantly increase the detection efficiency, with gains of 7,4% to 9,8% in terms of both precision and recall rates. Moreover, in order to reduce the computational complexity, a two-pass analysis is performed. For each detected shot we propose a leap keyframe extraction method that generates static summaries. Finally, the detected keyframes feed a novel shot clustering algorithm which integrates a set of temporal constraints. Video scenes are obtained with average precision and recall rates of 85%. © 2011 Springer-Verlag.
CITATION STYLE
Tapu, R., & Zaharia, T. (2011). High level video temporal segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6938 LNCS, pp. 224–235). https://doi.org/10.1007/978-3-642-24028-7_21
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