Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Affect-based indexing and retrieval of films
Proceedings of the 13th annual 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
Proceedings of the 15th international conference on Multimedia
Letters: Adaptive local hyperplane classification
Neurocomputing
Hierarchical movie affective content analysis based on arousal and valence features
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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
Affective video content representation and modeling
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
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Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying machine learning methods. Therefore, choosing effective features and developing efficient machine learning algorithms become vital for affective analysis. In this paper, we investigate the effectiveness of a novel classification approach, called Adaptive Local Hyperplanes (ALH), in affective analysis. The reason ALH is appealing in affective analysis is two-fold. Firstly, affective features are not equally important for emotion categories; ALH inherently assigns feature weights based on discriminative ability of each feature. Secondly, ALH achieves competitive performance with state-of-the-art classifiers (e.g., SVM) while it is designed for multi-class classification. Consequently, it is worthwhile to explore the usage of ALH in affective analysis. MTV data are used in this study. As the first effort of applying ALH to affective analysis, the results presented in this paper provide a foundation for future research in affective analysis.