Affective MTV analysis based on arousal and valence features

Nowadays, MTV has become an important favorite pastime to modern people because of its conciseness, convenience to play and the characteristic that can bring both audio and visual experiences to audiences. In this paper, we propose an affective MTV analysis framework, which realizes MTV affective state extraction, representation and clustering. Firstly, affective features are extracted from both audio and visual signals. Then, the affective state of each MTV is modeled with 2D dimensional affective model and visualized in the Arousal-Valence space. Finally the MTVs having similar affective states are clustered into same categories. The validity of proposed framework is proved by subjective user study. The comparisons between our selected features and those in related work prove that our features improve the performance by a significant margin.