Flexible presentation of videos based on affective content analysis

The explosion of multimedia contents has resulted in a great demand of video presentation. While most previous works focused on presenting certain type of videos or summarizing videos by event detection, we propose a novel method to present general videos of different genres based on affective content analysis. We first extract rich audio-visual affective features and select discriminative ones. Then we map effective features into corresponding affective states in an improved categorical emotion space using hidden conditional random fields (HCRFs). Finally we draw affective curves which tell the types and intensities of emotions. With the curves and related affective visualization techniques, we select the most affective shots and concatenate them to construct affective video presentation with a flexible and changeable type and length. Experiments on representative video database from the web demonstrate the effectiveness of the proposed method.