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From Video Shot Clustering to Sequence Segmentation

Emmanuel Veneau 1 Rémi Ronfard 2, * Patrick Bouthemy 1
* Corresponding author
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Segmenting video documents into sequences from elementary shots to supply an appropriate higher level description of the video is a challenging task. This paper presents a two-stage method. First, we build a binary agglomerative hierarchical time-constrained shot clustering. Second, based on the cophenetic criterion, a breaking distance between shots is computed to detect sequence changes. Various options are implemented and compared. Real experiments have proved that the proposed criterion can be efficiently used to achieve appropriate segmentation into sequences.
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https://hal.inria.fr/inria-00545119
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Last modification on : Saturday, July 11, 2020 - 3:14:31 AM
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Emmanuel Veneau, Rémi Ronfard, Patrick Bouthemy. From Video Shot Clustering to Sequence Segmentation. International Conference on Pattern Recognition, IEEE, 2000, Barcelone, Spain. pp.254-257, ⟨10.1109/ICPR.2000.902907⟩. ⟨inria-00545119⟩

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