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标 题: Video Affective Content Representation and Recognition Using Video Affective Tree and Hidden Markov Models(EI)
作 者: Kai Sun, Junqing Yu
会议 / 期刊: Proceeding of the 2nd International Conference on Affective Computing and Intell
发 表 时 间: 2007年
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论文摘要
A video affective content representation and recognition framework based on Video Affective Tree (VAT) and Hidden Markov Mode
ls (HMMs) is presented. Video affective content units in different granularities are firstly located by excitement intensity curv
es, and then the selected affective content units are used to construct VAT. According to the excitement intensity curve the affe
ctive intensity of each affective content unit at different levels of VAT can also be quantified into several levels from weak to
strong. Many middlelevel audio and visual affective features, which represent emotional characteristics, are designed and extrac
ted to construct observation vectors. Based on these observation vector sequences HMMs-based video affective content recognizers
are trained and tested to recognize the basic emotional events of audience (joy, anger, sadness and fear). The experimental resul
ts show that the proposed framework is not only suitable for a broad range of video affective understanding applications, but als
o capable of representing affective semantics in different granularities.

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