Classification of general audio data for content-based retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hierarchical movie affective content analysis based on arousal and valence features
MM '08 Proceedings of the 16th ACM international conference on Multimedia
i.MTV: an integrated system for mtv affective analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Emotion-based music retrieval on a well-reduced audio feature space
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
An improved valence-arousal emotion space for video affective content representation and recognition
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Utilizing affective analysis for efficient movie browsing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Affective video content representation and modeling
IEEE Transactions on Multimedia
Affective Level Video Segmentation by Utilizing the Pleasure-Arousal-Dominance Information
IEEE Transactions on Multimedia
Affective understanding in film
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive local hyperplanes for MTV affective analysis
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Learning representations for affective video understanding
Proceedings of the 21st ACM international conference on Multimedia
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Music video is a popular type of entertainment by viewers. Currently, the novel indexing and retrieval approach based on the affective cues contained in music videos becomes more and more attractive to users. Music video affective analysis and understanding is one of the most popular topics in current multimedia community. In this paper, we propose a novel feature importance analysis approach to select most representative arousal and valence features for arousal and valence modeling. Compared with state-of-the-art work by Zhang on music video affective analysis, our main contributions are in the following aspects: (1) Another 3 affect-related features are extracted to enrich the feature set and exploit their correlation with arousal and valence. (2) All extracted features are ordered via feature importance analysis, and then optimal feature subset is selected after ordering. (3) Different regression methods are compared for arousal and valence modeling in order to find the fittest estimation function. Our method achieves 33.39% and 42.17% deduction in terms of mean absolute error compared with Zhang's method. Experimental results demonstrate our proposed method has a considerable improvement on music video affective understanding.