Latent topic driving model for movie affective scene classification
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Music video affective understanding using feature importance analysis
Proceedings of the ACM International Conference on Image and Video Retrieval
Correlation-based feature selection and regression
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
Image and Vision Computing
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In this paper, we offer an entirely new view to the problem of high level video parsing. We developed a novel computation method for affective level video segmentation. Its function was to extract emotional segments from videos. Its design was based on the pleasure-arousal-dominance (P-A-D) model of affect representation , which in principle can represent a large number of emotions. Our method had two stages. The first P-A-D estimation stage was defined within framework of the dynamic Bayesian networks (DBNs). A spectral clustering algorithm was applied in the final stage to determine the emotional segments of the video. The performance of our method was compared with the time adaptive clustering (TAC) algorithm and an accelerated version of it which we had developed. According to Vendrig , the TAC algorithm was the best segmentation method. Experiment results will show the feasibility of our method.