Using context saliency for movie shot classification

Xu, M; Wang, J; Hasan, MA; He, X; Xu, C; Lu, H; Jin, JS

Using context saliency for movie shot classification

Xu, M

Wang, J Hasan, MA He, X

Xu, C Lu, H Jin, JS

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2011, pp. 3653 - 3656
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Hasan, MA	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Lu, H	en_US
dc.contributor.author	Jin, JS	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2011, pp. 3653 - 3656	en_US
dc.identifier.isbn	9781457713033	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29401
dc.description.abstract	Movie shot classification is vital but challenging task due to various movie genres, different movie shooting techniques and much more shot types than other video domain. Variety of shot types are used in movies in order to attract audiences attention and enhance their watching experience. In this paper, we introduce context saliency to measure visual attention distributed in keyframes for movie shot classification. Different from traditional saliency maps, context saliency map is generated by removing redundancy from contrast saliency and incorporating geometry constrains. Context saliency is later combined with color and texture features to generate feature vectors. Support Vector Machine (SVM) is used to classify keyframes into pre-defined shot classes. Different from the existing works of either performing in a certain movie genre or classifying movie shot into limited directing semantic classes, the proposed method has three unique features: 1) context saliency significantly improves movie shot classification; 2) our method works for all movie genres; 3) our method deals with the most common types of video shots in movies. The experimental results indicate that the proposed method is effective and efficient for movie shot classification. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2011.6116510	en_US
dc.title	Using context saliency for movie shot classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Brussels, Belgium	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Movie shot classification is vital but challenging task due to various movie genres, different movie shooting techniques and much more shot types than other video domain. Variety of shot types are used in movies in order to attract audiences attention and enhance their watching experience. In this paper, we introduce context saliency to measure visual attention distributed in keyframes for movie shot classification. Different from traditional saliency maps, context saliency map is generated by removing redundancy from contrast saliency and incorporating geometry constrains. Context saliency is later combined with color and texture features to generate feature vectors. Support Vector Machine (SVM) is used to classify keyframes into pre-defined shot classes. Different from the existing works of either performing in a certain movie genre or classifying movie shot into limited directing semantic classes, the proposed method has three unique features: 1) context saliency significantly improves movie shot classification; 2) our method works for all movie genres; 3) our method deals with the most common types of video shots in movies. The experimental results indicate that the proposed method is effective and efficient for movie shot classification. © 2011 IEEE.

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http://hdl.handle.net/10453/29401