Article

Application of computational media aesthetics methodology to extracting color semantics in film

Authors:

Ba Tu Truong,

Svetha Venkatesh,

Chitra DoraiAuthors Info & Claims

MULTIMEDIA '02: Proceedings of the tenth ACM international conference on Multimedia

Pages 339 - 342

https://doi.org/10.1145/641007.641079

Published: 01 December 2002 Publication History

Get Access

Abstract

Using film grammar as the underpinning, we study the extraction of structures in video based on color using a wide configuration of clustering methods combined with existing and new similarity measures. We study the visualisation of these structures, which we call Scene-Cluster Temporal Charts and show how it can bring out the interweaving of different themes and settings in a film. We also extract color events that filmmakers use to draw/force a viewer's attention to a shot/scene. This is done by first extracting a set of colors used rarely in film, and then building a probabilistic model for color event detection. We demonstrate with experimental results from ten movies that our algorithms are effective in the extraction of both scene-cluster temporal charts and color events.

References

[1]

C. Colombo, A. D. Bimbo, and P. Pala. Semantics in information retrieval. IEEE Multimedia, 6(3):38--53, 1999.

Digital Library

Google Scholar

[2]

C. Dorai and S. Venkatesh. Computational Media Aesthetics: Finding meaning beautiful. IEEE Multimedia, 8(4):10--12, October-December 2001.

Digital Library

Google Scholar

[3]

B. S. Everitt. Cluster Analysis. Edward Arnold, 3rd edition, 1993.

Google Scholar

[4]

W. Johnson. Coming to term with color. Film Quaterly, 20(1):2--22, 1996.

Google Scholar

[5]

M. R. Napahde and T. S. Huang. A probablistics framework for semantic video indexing, filtering and retrieval. IEEE Transactions on Multimedia, 3(1):141--151, March 2001.

Digital Library

Google Scholar

[6]

S. Neale. Cinema and Technology: Image, Sound and Color. MacMillan Education Ltd, 1985.

Google Scholar

[7]

B. T. Truong, C. Dorai, and S. Venkatesh. Automatic scene extraction in motion pictures. Technical Report 1/2001, School of Computing, Curtin University of Technology, Perth, Western Australia, 2001.

Google Scholar

[8]

H. Zettl. Sight Sound Motion: Applied Media Aesthetics. Wadsworth Publishing, 3rd edition, 1999.

Google Scholar

Cited By

View all

Silva P(2012)Classification, Segmentation and Chronological Prediction of Cinematic SoundProceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2012.172(369-374)Online publication date: 12-Dec-2012
https://dl.acm.org/doi/10.1109/ICMLA.2012.172
Cristani MPesarin ADrioli CMurino VRodà AGrapulin MSebe Ndel Bimbo AChang SSmeulders A(2010)Toward an automatically generated soundtrack from low-level cross-modal correlations for automotive scenariosProceedings of the 18th ACM international conference on Multimedia10.1145/1873951.1874024(551-560)Online publication date: 25-Oct-2010
https://dl.acm.org/doi/10.1145/1873951.1874024
Manzato MCoimbra DGoularte RDe Grooff DCesar PGeerts D(2009)Multimedia content personalization based on peer-level annotationProceedings of the 7th European Conference on Interactive TV and Video10.1145/1542084.1542096(57-66)Online publication date: 3-Jun-2009
https://dl.acm.org/doi/10.1145/1542084.1542096
Show More Cited By

Index Terms

Application of computational media aesthetics methodology to extracting color semantics in film
1. Information systems
  1. Information retrieval
    1. Document representation

Recommendations

Computational intelligence techniques for colour clustering
ICCC '11: Proceedings of the 2011 International Conference on Innovative Computing and Cloud Computing

Colour clustering (or colour quantisation) is an elementary image processing process to reduce the number of distinct colours in an image. Colour quantised images contain a colour palette and it is the entries in this palette that determine the quality ...
Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation

In this paper, a new and efficient edge-preserving algorithm is presented for color contrast enhancement in CIE Lu^'v^' color space. The proposed algorithm not only can enhance the color contrast as the previous algorithm does, but also has an edge-...
Computational Color Constancy
ICPR '00: Proceedings of the International Conference on Pattern Recognition - Volume 1

The color response of a camera or of the human eye is confounded by illumination. The colors recorded under yellow and blue illuminants are respectively more yellowish and bluish than they ought to be. Removing color bias due to illumination is called ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MULTIMEDIA '02: Proceedings of the tenth ACM international conference on Multimedia

December 2002

683 pages

ISBN:158113620X

DOI:10.1145/641007

Conference Chair:
Lawrence Rowe
UC Berkeley
,
General Chair:
Bernard Merialdo
Institut EURECOM
,
Program Chairs:
Max Muhlhauser
TU Darmstadt
,
Keith Ross
Institut EURECOM
,
Nevenka Dimitrova
Phillips Research

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2002

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

MM02

Sponsor:

MM02: ACM Multimedia 2002

December 1 - 6, 2002

Juan-les-Pins, France

Acceptance Rates

MULTIMEDIA '02 Paper Acceptance Rate 46 of 330 submissions, 14%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
579
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)5

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Silva P(2012)Classification, Segmentation and Chronological Prediction of Cinematic SoundProceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2012.172(369-374)Online publication date: 12-Dec-2012
https://dl.acm.org/doi/10.1109/ICMLA.2012.172
Cristani MPesarin ADrioli CMurino VRodà AGrapulin MSebe Ndel Bimbo AChang SSmeulders A(2010)Toward an automatically generated soundtrack from low-level cross-modal correlations for automotive scenariosProceedings of the 18th ACM international conference on Multimedia10.1145/1873951.1874024(551-560)Online publication date: 25-Oct-2010
https://dl.acm.org/doi/10.1145/1873951.1874024
Manzato MCoimbra DGoularte RDe Grooff DCesar PGeerts D(2009)Multimedia content personalization based on peer-level annotationProceedings of the 7th European Conference on Interactive TV and Video10.1145/1542084.1542096(57-66)Online publication date: 3-Jun-2009
https://dl.acm.org/doi/10.1145/1542084.1542096
Venkatesh SAdams BDinh Phung Dorai CFarrell RAgnihotri LDimitrova N(2008)“You Tube and I Find”—Personalizing Multimedia Content AccessProceedings of the IEEE10.1109/JPROC.2008.91637896:4(697-711)Online publication date: Apr-2008
https://doi.org/10.1109/JPROC.2008.916378
Truong BVenkatesh S(2007)Linking identities and viewpoints in home movies based on robust feature matchingProceedings of the 13th international conference on Multimedia Modeling - Volume Part I10.1007/978-3-540-69423-6_62(636-648)Online publication date: 9-Jan-2007
https://dl.acm.org/doi/10.1007/978-3-540-69423-6_62
Achanta RYan WKankanhalli M(2006)Modeling Intent for Home Video RepurposingIEEE MultiMedia10.1109/MMUL.2006.1213:1(46-55)Online publication date: 1-Jan-2006
https://dl.acm.org/doi/10.1109/MMUL.2006.12
Truong BVenkatesh SDorai C(2005)Extraction of Film Takes for Cinematic AnalysisMultimedia Tools and Applications10.1007/s11042-005-0892-z26:3(277-298)Online publication date: 1-Aug-2005
https://dl.acm.org/doi/10.1007/s11042-005-0892-z
Ba Tu Truong Venkatesh SDorai C(2004)Discovering semantics from visualizations of film takes10th International Multimedia Modelling Conference, 2004. Proceedings.10.1109/MULMM.2004.1264974(109-116)Online publication date: 2004
https://doi.org/10.1109/MULMM.2004.1264974
Cheng-Yu Wei Dimitrova NShih-Fu Chang (2004)Color-mood analysis of films based on syntactic and psychological models2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763)10.1109/ICME.2004.1394329(831-834)Online publication date: 2004
https://doi.org/10.1109/ICME.2004.1394329
Yazhong Feng Yueting Zhuang Yunhe Pan (2003)Music information retrieval by detecting mood via computational media aestheticsProceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)10.1109/WI.2003.1241199(235-241)Online publication date: 2003
https://doi.org/10.1109/WI.2003.1241199
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Computational intelligence techniques for colour clustering

Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation

Computational Color Constancy