Affective computing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Affective video content representation and modeling
IEEE Transactions 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
Affect-based adaptive presentation of home videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Affective acoustic ecology: towards emotionally enhanced sound events
Proceedings of the 7th Audio Mostly Conference: A Conference on Interaction with Sound
Image and Vision Computing
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To understand video affective content automatically, the primary task is to transform the abstract concept of emotion into the form which can be handled by the computer easily. An improved V-A emotion space is proposed to address this problem. It unifies the discrete and dimensional emotion model by introducing the typical fuzzy emotion subspace. Fuzzy C-mean clustering (FCM) algorithm is adopted to divide the V-A emotion space into the subspaces and Gaussian mixture model (GMM) is used to determine their membership functions. Based on the proposed emotion space, the maximum membership principle and the threshold principle are introduced to represent and recognize video affective content. A video affective content database is created to validate the proposed model. The experimental results show that the improved emotion space can be used as a solution to represent and recognize video affective content.