Motion-Based Video Representation for Scene Change Detection
International Journal of Computer Vision
Audio-Visual Event Detection using Duration dependent input output Markov models
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Semantic Analysis and Retrieval of Sports Video
FCST '06 Proceedings of the Japan-China Joint Workshop on Frontier of Computer Science and Technology
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Content-based video indexing of TV broadcast news using hidden Markov models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Affective video content representation and modeling
IEEE Transactions on Multimedia
Video partitioning by temporal slice coherency
IEEE Transactions on Circuits and Systems for Video Technology
Affect-based adaptive presentation of home videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Affective content analysis of music video clips
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Video affective content recognition based on genetic algorithm combined HMM
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
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A video affective content representation and recognition framework based on Video Affective Tree (VAT) and Hidden Markov Models (HMMs) is presented. Video affective content units in different granularities are firstly located by excitement intensity curves, and then the selected affective content units are used to construct VAT. According to the excitement intensity curve the affective intensity of each affective content unit at different levels of VAT can also be quantified into several levels from weak to strong. Many middle-level audio and visual affective features, which represent emotional characteristics, are designed and extracted to construct observation vectors. Based on these observation vector sequences HMMs-based video affective content recognizers are trained and tested to recognize the basic emotional events of audience (joy, anger, sadness and fear). The experimental results show that the proposed framework is not only suitable for a broad range of video affective understanding applications, but also capable of representing affective semantics in different granularities.