This paper addresses the problem of unsupervised TV program structuring. Program structuring allows direct and non linear access to the desired parts of a program. Our work addresses the structuring of programs like news, entertainment, shows, magazines... It is based on the detection of audio and visual recurrences. It proposes an effective classification and selection system, based on decision trees, that allows the detection of "separators" among these recurrences. Separators are short audio/visual sequences that delimit the different parts of a program. The decision trees are built based on attributes issued from techniques like applause detection, scenes segmentation, face/speaker detection and clustering. The approach has been evaluated on a 112 hours dataset corresponding to 169 episodes of TV programs.
Audio/visual recurrences and decision trees for unsupervised TV program structuring
VISAPP 2013, 8th International Joint Conference on Computer Vision Theory and Applications, 21-24 February 2013, Barcelona, Spain
Type:
Conference
City:
Barcelona
Date:
2013-02-21
Department:
Data Science
Eurecom Ref:
3914
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in VISAPP 2013, 8th International Joint Conference on Computer Vision Theory and Applications, 21-24 February 2013, Barcelona, Spain and is available at :
See also:
PERMALINK : https://www.eurecom.fr/publication/3914
