Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A tutorial on support vector regression
Statistics and Computing
Proceedings of the 15th international conference on Multimedia
Hierarchical movie affective content analysis based on arousal and valence features
MM '08 Proceedings of the 16th ACM international conference on Multimedia
RoleNet: movie analysis from the perspective of social networks
IEEE Transactions on Multimedia - Special issue on integration of context and content
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
A flexible framework for key audio effects detection and auditory context inference
IEEE Transactions on Audio, Speech, and Language Processing
Affective video content representation and modeling
IEEE Transactions on Multimedia
Affective understanding in film
IEEE Transactions on Circuits and Systems for Video Technology
Music video affective understanding using feature importance analysis
Proceedings of the ACM International Conference on Image and Video Retrieval
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
Affect-based adaptive presentation of home videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
The aMotion toolkit: painting with affective motion textures
CAe '12 Proceedings of the Eighth Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging
The acousticvisual emotion guassians model for automatic generation of music video
Proceedings of the 20th ACM international conference on Multimedia
Expression analysis in the wild: from individual to groups
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
| Hi-index | 0.00 |
Because of the fast increasing number of movies and long time span each movie lasts, novel methods should be developed to help users browse movies and find their desired clips effectively. Affective information in movies is closely related with users' experiences and preferences. Therefore, in this paper, we analyze the affective states of movies and propose affective information based movie browsing. Affective movie content analysis is challenging due to the great variety of movie contents and styles. To address this challenge, we first extract rich audio-visual features. Then, feature selection and affective modeling are carried out to select and map effective features into corresponding affective states. Finally, we propose novel Affective Visualization techniques which intuitively visualize affective states to achieve efficient and user-friendly movie browsing. Experiments on representative movie dataset demonstrate the effectiveness of our proposed methods.