Fundamentals of speech recognition
Fundamentals of speech recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Creating audio keywords for event detection in soccer video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Affective video content representation and modeling
IEEE Transactions on Multimedia
Audio-Visual Affect Recognition
IEEE Transactions on Multimedia
On the use of computable features for film classification
IEEE Transactions on Circuits and Systems for Video Technology
Affective understanding in film
IEEE Transactions on Circuits and Systems for Video Technology
Latent topic driving model for movie affective scene classification
MM '09 Proceedings of the 17th ACM international conference on Multimedia
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Music video affective understanding using feature importance analysis
Proceedings of the ACM International Conference on Image and Video Retrieval
Utilizing affective analysis for efficient movie browsing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Dynamic captioning: video accessibility enhancement for hearing impairment
Proceedings of the international conference on Multimedia
Using scripts for affective content retrieval
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Adaptive local hyperplanes for MTV affective analysis
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Affective classification in video based on semi-supervised learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Video accessibility enhancement for hearing-impaired users
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Learning representations for affective video understanding
Proceedings of the 21st ACM international conference on Multimedia
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Emotional factors directly reflect audiences' attention, evaluation and memory. Affective contents analysis not only create an index for users to access their interested movie segments, but also provide feasible entry for video highlights. Most of the work focus on emotion type detection. Besides emotion type, emotion intensity is also a significant clue for users to find their interested content. For some film genres (Horror, Action, etc), the segments with high emotion intensity have the most possibilities to be video highlights. In this paper, we propose a hierarchical structure for emotion categories and analyze emotion intensity and emotion type by using arousal and valence related features hierarchically. Firstly, High, Medium and Low are detected as emotion intensity levels by using fuzzy c-mean clustering on arousal features. Fuzzy clustering provides a mathematical model to represent vagueness, which is close to human perception. After that, valence related features are used to detect emotion types (Anger, Sad, Fear, Happy and Neutral). Considering video is continuous time series data and the occurrence of a certain emotion is affected by recent emotional history, Hidden Markov Models (HMMs) are used to capture the context information. Experimental results shows the movie segments with high emotion intensity cover over 80% of the movie highlights in Horror and Action movies and the hierarchical method outperforms the one-step method on emotion type detection. Meanwhile, it is flexible for user to pick up their favorite affective content by choosing both emotion intensity levels and emotion types.