Neurocomputing
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning of event segmentation for news on demand
Communications of the ACM
Content-based indexing and retrieval of TV news
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Efficient matching and clustering of video shots
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Classifying Audio of Movies by a Multi-Expert System
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Efficient MPEG compressed video analysis using macroblock typeinformation
IEEE Transactions on Multimedia
A highly efficient system for automatic face region detection in MPEG video
IEEE Transactions on Circuits and Systems for Video Technology
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
A Multi-expert System for Movie Segmentation
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
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In this paper we propose a method for the detection of dialogue scenes within movies. This task is of particular interest given the special semantic role played by dialogue based scenes in the most part of movies. The proposed approach firstly operates the segmentation of the video footage in shots, then each shot is classified as dialogue or not-dialogue by a Multi-Expert System (MES) and, finally, the individuated sequences of dialogue shots are aggregated in dialogue scenes by means of a suitable algorithm. The MES integrates three experts which consider different and complementary aspects of the same decision problem, so that the combination of the single decisions provides a performance that is better than that of any single expert. While the general approach of multiple experts is not new, its application to this specific problem is interesting and novel and the obtained results are encouraging.