A close-up detection method for movies

Liu, H; Xu, M; Huang, Q; Jin, JS; Jiang, S; Xu, C

A close-up detection method for movies

Liu, H Xu, M

Huang, Q Jin, JS Jiang, S Xu, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2010, pp. 1505 - 1508
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Liu, H	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Huang, Q	en_US
dc.contributor.author	Jin, JS	en_US
dc.contributor.author	Jiang, S	en_US
dc.contributor.author	Xu, C	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2010, pp. 1505 - 1508	en_US
dc.identifier.isbn	9781424479948	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16168
dc.description.abstract	Close-up (CU) is a photographic technique which tightly frames a person or an object. In movies, it is applied to guide audience attention and to evoke audience emotion. In this paper, we detect face CU, object CU, and lean of movies, which are widely used to romance emotions. A lean consists of shots in a sequence, with a close-up shot as focus. A set of features are extracted by considering movie making techniques and human attention for CU detection. The features are average saliency, color entropy, color variance, face height, skin area, and texture scales. These features are tested through statistical hypothesis test to be significantly discriminating for CUs. Then, Support Vector Machine (SVM) is applied on these features to detect face CU and object CU. Based on the face CU and object CU detection result, lean is further detected by investigating the changing of the face/object size. Lean detection is of challenge due to the technique of montage. We solve this problem through color similarity estimation and SIFT point matching. Experimental results on four full length movies verify the effectiveness of the proposed method. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2010.5652408	en_US
dc.title	A close-up detection method for movies	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
dc.location.activity	Hongkong	en_US
dc.location.activity	ISI:000280583700007
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 - 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:

Close-up (CU) is a photographic technique which tightly frames a person or an object. In movies, it is applied to guide audience attention and to evoke audience emotion. In this paper, we detect face CU, object CU, and lean of movies, which are widely used to romance emotions. A lean consists of shots in a sequence, with a close-up shot as focus. A set of features are extracted by considering movie making techniques and human attention for CU detection. The features are average saliency, color entropy, color variance, face height, skin area, and texture scales. These features are tested through statistical hypothesis test to be significantly discriminating for CUs. Then, Support Vector Machine (SVM) is applied on these features to detect face CU and object CU. Based on the face CU and object CU detection result, lean is further detected by investigating the changing of the face/object size. Lean detection is of challenge due to the technique of montage. We solve this problem through color similarity estimation and SIFT point matching. Experimental results on four full length movies verify the effectiveness of the proposed method. © 2010 IEEE.

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http://hdl.handle.net/10453/16168