Semantic-event based analysis and segmentation of wedding ceremony videos
Proceedings of the international workshop on Workshop on multimedia information retrieval
A User Experience Model for Home Video Summarization
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
An interactive and multi-level framework for summarising user generated videos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
VideoSense: a contextual in-video advertising system
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the international conference on Multimedia
Automatically protecting privacy in consumer generated videos using intended human object detector
Proceedings of the international conference on Multimedia
A user-centric system for home movie summarisation
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Extracting intentionally captured regions using point trajectories
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Interactive and real-time generation of home video summaries on mobile devices
IMMPD '11 Proceedings of the 2011 international ACM workshop on Interactive multimedia on mobile and portable devices
Social interaction detection using a multi-sensor approach
Proceedings of the 21st ACM international conference on Multimedia
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With the rapid adoption of consumer digital video recorders and an increase of home video data, content analysis has become an interesting and key research issue to provide personalized experiences and services for both camcorder users and viewers. In this paper, we present a novel view to tackle this issue, which aims at modeling and mining of the capture intention of camcorder users. Based on the study of intention mechanism in psychology, a set of domain-specific capture intention concepts is defined. A comprehensive and extensible scheme consisting of video structure decomposition, intention-oriented feature analysis, as well as singular-value-decomposition-based intention segmentation and learning-based intention classification is proposed to mine the users' capture intention. Experiments were carried on home video sequences of 90 h in total, taken by 16 persons over the past 20 years. Both the user study and objective evaluations indicate that our proposed intention-based approach is an effective complement to existing home video content analysis schemes