Manaus, AM, Brazil
Oct. 8, 2006 to Oct. 11, 2006
G. Camara Chavez , Equipe Traitment des Images et du Signal-ENSEA
F. Precioso , Equipe Traitment des Images et du Signal-ENSEA
S. Philipp-Foliguet , Equipe Traitment des Images et du Signal-ENSEA
Arnaldo de A. Araujo , Federal University of Minas Gerais
In most of video shot boundary detection algorithms, proposed in the literature, several parameters and thresholds have to be set in order to achieve good results. In this paper, to get rid of parameters and thresholds, we explore a supervised classification method for video shot segmentation. We transform the temporal segmentation into a class categorization issue. Our approach defines a uniform framework for combining different kinds of features extracted from the video. Our method does not require any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. The experiments, following strictly the TRECVID 2002 competition protocol, provide very good results dealing with a large amount of features thanks to our kernel-based SVM classification method.
G. Camara Chavez, F. Precioso, S. Philipp-Foliguet, Arnaldo de A. Araujo, "Video Segmentation by Supervised Learning", SIBGRAPI, 2006, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images 2006, pp. 365-372, doi:10.1109/SIBGRAPI.2006.48