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- W2031777151 abstract "This paper presents a novel content-based system that utilizes 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 relationship 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 similarity between the two corresponding emotion distributions, based on a distance measure such as KL divergence." @default.
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- W2031777151 date "2012-10-29" @default.
- W2031777151 modified "2023-09-23" @default.
- W2031777151 title "The acousticvisual emotion guassians model for automatic generation of music video" @default.
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- W2031777151 doi "https://doi.org/10.1145/2393347.2396494" @default.
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