NDLI: A Novel Probabilistic Approach to Modeling the Pleasure-Arousal-Dominance Content of the Video based on "Working Memory"

Content Provider	IEEE Xplore Digital Library
Author	Arifin, S. Cheung, P.Y.K.
Copyright Year	2007
Description	Author affiliation: Imperial Coll. London, London (Arifin, S.; Cheung, P.Y.K.)
Abstract	The rapid growth of digital video information nowadays is making video content classification and indexing tools a necessity. Little research efforts have been invested on content classification solutions based on the emotional content of the video, which have the potential of extending the scope of video indexing possibilities. The development of an affective content classification solution faces a number of challenges. These include the dynamic and time evolving nature of the video's emotional content and the uncertainty in the multimodal sensory observations. These challenges have resulted in the lack of reliable methods for video emotional content modeling. This paper introduce a novel probabilistic approach to model the emotional content of the video based on the dynamic Bayesian networks (DBNs). It is designed based on the pleasure-arousal-dominance (P-A-D) emotion model, which in principle can represent a large number of emotions. It is also designed based on the concept of "working memory", a theoretical framework within cognitive psychology that refers to the structures and processes used for temporarily storing and manipulating information. Our experiment results demonstrate that "working memory" is an important aiding factor in complex emotional content analysis.
Starting Page	147
Ending Page	154
File Size	596443
Page Count	8
File Format	PDF
ISBN	9780769529974
DOI	10.1109/ICSC.2007.22
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Emotion recognition Gunshot detection systems Uncertainty Dynamic Psychology Data mining Videoconference emotion recognition. Bayesian methods Layout Video compression Affective video content analysis Bayesian networks Indexing
Content Type	Text
Resource Type	Article

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