Automatic rating of movies using an arousal curve extracted from video features

This paper discusses the extraction of film structure features from action films to build an arousal curve. The arousal curve is used as training data for building a Hidden Markov Model for predicting the rating of a movie. Evaluation of the model resulted in a 70% accuracy, which shows that there i...

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Main Authors:	Tan, Daniel Stanley, See, Solomon, Tiam-Lee, Thomas James Z.
Format:	text
Published:	Animo Repository 2014
Subjects:	Motion pictures—Evaluation—Automation Image processing Computer Sciences
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/1858 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2857/type/native/viewcontent/HNICEM.2014.7016211
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Institution:	De La Salle University

id	oai:animorepository.dlsu.edu.ph:faculty_research-2857
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spelling	oai:animorepository.dlsu.edu.ph:faculty_research-28572024-08-19T08:30:19Z Automatic rating of movies using an arousal curve extracted from video features Tan, Daniel Stanley See, Solomon Tiam-Lee, Thomas James Z. This paper discusses the extraction of film structure features from action films to build an arousal curve. The arousal curve is used as training data for building a Hidden Markov Model for predicting the rating of a movie. Evaluation of the model resulted in a 70% accuracy, which shows that there is some form of correlation between the structure of a film and its perceived rating. Interesting similarities were also observed in the arousal curve patterns between different movies in the same classifications. © 2014 IEEE. 2014-11-01T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/1858 info:doi/10.1109/HNICEM.2014.7016211 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2857/type/native/viewcontent/HNICEM.2014.7016211 Faculty Research Work Animo Repository Motion pictures—Evaluation—Automation Image processing Computer Sciences
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
topic	Motion pictures—Evaluation—Automation Image processing Computer Sciences
spellingShingle	Motion pictures—Evaluation—Automation Image processing Computer Sciences Tan, Daniel Stanley See, Solomon Tiam-Lee, Thomas James Z. Automatic rating of movies using an arousal curve extracted from video features
description	This paper discusses the extraction of film structure features from action films to build an arousal curve. The arousal curve is used as training data for building a Hidden Markov Model for predicting the rating of a movie. Evaluation of the model resulted in a 70% accuracy, which shows that there is some form of correlation between the structure of a film and its perceived rating. Interesting similarities were also observed in the arousal curve patterns between different movies in the same classifications. © 2014 IEEE.
format	text
author	Tan, Daniel Stanley See, Solomon Tiam-Lee, Thomas James Z.
author_facet	Tan, Daniel Stanley See, Solomon Tiam-Lee, Thomas James Z.
author_sort	Tan, Daniel Stanley
title	Automatic rating of movies using an arousal curve extracted from video features
title_short	Automatic rating of movies using an arousal curve extracted from video features
title_full	Automatic rating of movies using an arousal curve extracted from video features
title_fullStr	Automatic rating of movies using an arousal curve extracted from video features
title_full_unstemmed	Automatic rating of movies using an arousal curve extracted from video features
title_sort	automatic rating of movies using an arousal curve extracted from video features
publisher	Animo Repository
publishDate	2014
url	https://animorepository.dlsu.edu.ph/faculty_research/1858 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2857/type/native/viewcontent/HNICEM.2014.7016211
_version_	1808617029606834176

Automatic rating of movies using an arousal curve extracted from video features

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