Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Action movies segmentation and summarization based on tempo analysis
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
Video visualization for compact presentation and fast browsing of pictorial content
IEEE Transactions on Circuits and Systems for Video Technology
Automatically selecting shots for action movie trailers
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Associating characters with events in films
Proceedings of the 6th ACM international conference on Image and video retrieval
Indexing of fictional video content for event detection and summarisation
Journal on Image and Video Processing
Speaker Clustering Aided by Visual Dialogue Analysis
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A framework for dialogue detection in movies
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
A system for event-based film browsing
TIDSE'06 Proceedings of the Third international conference on Technologies for Interactive Digital Storytelling and Entertainment
Role-based identity recognition for TV broadcasts
Multimedia Tools and Applications
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Dialogue sequences constitute an important part of any movie or television program and their successful detection is an essential step in any movie summarisation/indexing system. The focus of this paper is to detect sequences of dialogue, rather than complete scenes. We argue that these shorter sequences are more desirable as retrieval units than temporally long scenes. This paper combines various audiovisual features that reflect accepted and well know film making conventions using a selection of machine learning techniques in order to detect such sequences. Three systems for detecting dialogue sequences are proposed: one based primarily on audio analysis, one based primarily on visual analysis and one that combines the results of both. The performance of the three systems are compared using a manually marked-up test corpus drawn from a variety of movies of different genres. Results show that high precision and recall can be obtained using low-level features that are automatically extracted.