The acousticvisual emotion Gaussians model for automatic generation of music video

This paper presents a novel content-based system that uti-lizes the perceived emotion of multimedia content as a bridge to connect music and video. Specifically, we propose a novel machine learning framework, called Acousticvisual Emotion Guassians (AVEG), to jointly learn the tripartite relation-ship among music, video, and emotion from an emotion-annotated corpus of music videos. For a music piece (or a video sequence), the AVEG model is applied to predict its emotion distribution in a stochastic emotion space from the corresponding low-level acoustic (resp. visual) features. Finally, music and video are matched by measuring the simi-larity between the two corresponding emotion distributions, based on a distance measure such as KL divergence