Utilizing affective analysis for efficient movie browsing

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.