Article

Affect-based indexing and retrieval of films

Authors:

Ching Hau Chan,

Gareth J. F. JonesAuthors Info & Claims

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

Pages 427 - 430

https://doi.org/10.1145/1101149.1101243

Published: 06 November 2005 Publication History

Get Access

Abstract

Digital multimedia systems are creating many new opportunities for rapid access to content archives. In order to explore these collections using search applications, the content must be annotated with significant features. An important and often overlooked aspect of human interpretation of multimedia data is the affective dimension. Affective labels of content can be extracted automatically from within multimedia data streams. These can then be used for content-based retrieval and browsing. In this study affective features extracted from multimedia audio content are mapped onto a set of keywords with predetermined emotional interpretations. These labels are then used to demonstrate affect-based retrieval on a range of feature films.

References

[1]

Smeaton, A.F., Lee, H. and McDonald, K.: Experiences of Creating Four Video Library Collections with the Fischlar System, International Journal on Digital Libraries, 4(10) (2004) 42--44

Digital Library

Google Scholar

[2]

Hauptmann, A.G., Christel, M.G.: Successful Approaches in the TREC Video Retrieval Evaluations, Proceedings of ACM Multimedia 2004, New York City, ACM (2004) 668--675

Digital Library

Google Scholar

[3]

Zhang, T., Kuo, C. -C. J.: Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing, Kluwer Academic Publishers, (2001)

Digital Library

Google Scholar

[4]

Hanjalic, A., Xu., L.-Q.: Affective Video Content Representation and Modeling, IEEE Transactions on Multimedia, 7(1) (2005) 143--154

Digital Library

Google Scholar

[5]

Chan, C. H., and Jones, G. J. F.: Annotation of Multimedia Audio Data with Affective Labels for Information Management, Proceedings of PRIS 2005 - 5th International Workshop on Pattern Recognition in Information Systems, Miami, USA (2005) 94--103

Google Scholar

[6]

Salway, A. and Graham, M.: Extracting Information about Emotions in Films, Proceedings of ACM Multimedia, Berkeley, ACM (2003) 299--302

Digital Library

Google Scholar

[7]

Russell, J., Mehrabian, A.: Evidence for a Three-Factor Theory of Emotions, Journal of Research in Personality, 11 (1977) 273--294

Crossref

Google Scholar

[8]

Bradley, M. M.: Emotional Memory: A Dimensional Analysis. In: van Groot, S., van de Poll, N.E., Sargeant, J. (eds.) The Emotions: Essays on Emotion Theory, Hillsdale, NJ: Erlbaum (1994) 97--134

Google Scholar

[9]

Dietz, R., Lang, A.: Affective Agents: Effects of Agent Affect on Arousal, Attention, Liking and Learning, Proceedings of the Third International Cognitive Technology Conference, San Francisco (1999)

Google Scholar

[10]

Ortony, A, Clore, G.L., Coolins, A.: The Cognitive Structure of Emotions, CUP (1988)

Google Scholar

[11]

Picard, R: Affective Computing, MIT Press (1997)

Crossref

Google Scholar

[12]

Luhn, H. P.: The Automatic Creation of Literature Abstracts, IBM Journal of Research and Development, 2(2) (1958) 159--165

Digital Library

Google Scholar

[13]

S. E. Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M., and M.Gatford, M.: Okapi at TREC-3. In D. K. Harman, editor, Proceedings of TREC-3, NIST (1995) 109--126

Google Scholar

Cited By

View all

Chen ZXiang XDeng XWang Q(2023)Stepwise Fusion Transformer for Affective Video Content AnalysisInternational Conference on Neural Computing for Advanced Applications10.1007/978-981-99-5847-4_27(375-386)Online publication date: 30-Aug-2023
https://doi.org/10.1007/978-981-99-5847-4_27
Piçarra NReis EChambel TArriaga P(2022)Searching, Navigating, and Recommending Movies through Emotions: A Scoping ReviewHuman Behavior and Emerging Technologies10.1155/2022/78310132022(1-24)Online publication date: 2-Dec-2022
https://doi.org/10.1155/2022/7831013
Wang QXiang XZhao JDeng X(2022)P2SL: Private-Shared Subspaces Learning for Affective Video Content Analysis2022 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME52920.2022.9859902(1-6)Online publication date: 18-Jul-2022
https://doi.org/10.1109/ICME52920.2022.9859902
Show More Cited By

Index Terms

Affect-based indexing and retrieval of films
1. Information systems
  1. Information retrieval

Recommendations

IR principles for content-based indexing and retrieval of functional brain images
CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management

In this paper, we explore the concept of a "library of brain images", which implies not only a repository of brain images, but also efficient search and retrieval mechanisms that are based on models derived from IR practice. As a preliminary study, we ...
Learning Similarity Matching in Multimedia Content-Based Retrieval

Many multimedia content-based retrieval systems allow query formulation with user setting of relative importance of features (e.g., color, texture, shape, etc) to mimic the user's perception of similarity. However, the systems do not modify their ...
An affect-based video retrieval system with open vocabulary querying
AMR'10: Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion

Content-based video retrieval systems (CBVR) are creating new search and browse capabilities using metadata describing significant features of the data. An often overlooked aspect of human interpretation of multimedia data is the affective dimension. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

November 2005

1110 pages

ISBN:1595930442

DOI:10.1145/1101149

General Chairs:
Hongjiang Zhang
Microsoft Research Asia, China
,
Tat-Seng Chua
National University of Singapore, Singapore
,
Program Chairs:
Ralf Steinmetz
Technische Universitat Darmstadt, Germany
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Lynn Wilcox
FXPAL

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: 06 November 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

MM05

Sponsor:

MM05: 2005 13th Annual ACM International Conference on Multimedia

November 6 - 11, 2005

Hilton, Singapore

Acceptance Rates

MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;

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

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

36
Total Citations
View Citations
548
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chen ZXiang XDeng XWang Q(2023)Stepwise Fusion Transformer for Affective Video Content AnalysisInternational Conference on Neural Computing for Advanced Applications10.1007/978-981-99-5847-4_27(375-386)Online publication date: 30-Aug-2023
https://doi.org/10.1007/978-981-99-5847-4_27
Piçarra NReis EChambel TArriaga P(2022)Searching, Navigating, and Recommending Movies through Emotions: A Scoping ReviewHuman Behavior and Emerging Technologies10.1155/2022/78310132022(1-24)Online publication date: 2-Dec-2022
https://doi.org/10.1155/2022/7831013
Wang QXiang XZhao JDeng X(2022)P2SL: Private-Shared Subspaces Learning for Affective Video Content Analysis2022 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME52920.2022.9859902(1-6)Online publication date: 18-Jul-2022
https://doi.org/10.1109/ICME52920.2022.9859902
Wang QXiang XZhao J(2022)ML-TFN: Multi Layers Tensor Fusion Network for Affective Video Content AnalysisNeural Computing for Advanced Applications10.1007/978-981-19-6142-7_14(184-196)Online publication date: 21-Oct-2022
https://doi.org/10.1007/978-981-19-6142-7_14
Ou YChen ZWu F(2021)Multimodal Local-Global Attention Network for Affective Video Content AnalysisIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.301488931:5(1901-1914)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1109/TCSVT.2020.3014889
Ding YHu XXia ZLiu YZhang D(2021)Inter-Brain EEG Feature Extraction and Analysis for Continuous Implicit Emotion Tagging During Video WatchingIEEE Transactions on Affective Computing10.1109/TAFFC.2018.284975812:1(92-102)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TAFFC.2018.2849758
Mokryn OBodoff DBader NAlbo YLanir J(2020)Sharing emotions: determining films’ evoked emotional experience from their online reviewsInformation Retrieval Journal10.1007/s10791-020-09373-1Online publication date: 9-May-2020
https://doi.org/10.1007/s10791-020-09373-1
Shepstone STan ZJensen S(2018)Audio-Based Granularity-Adapted Emotion ClassificationIEEE Transactions on Affective Computing10.1109/TAFFC.2016.25987419:2(176-190)Online publication date: 1-Apr-2018
https://doi.org/10.1109/TAFFC.2016.2598741
Soleimani SLaw EMival OSmyth MDalsgaard P(2017)What Can Self-Reports and Acoustic Data Analyses on Emotions Tell Us?Proceedings of the 2017 Conference on Designing Interactive Systems10.1145/3064663.3064770(489-501)Online publication date: 10-Jun-2017
https://dl.acm.org/doi/10.1145/3064663.3064770
Wang SJi Q(2015)Video Affective Content Analysis: A Survey of State-of-the-Art MethodsIEEE Transactions on Affective Computing10.1109/TAFFC.2015.24327916:4(410-430)Online publication date: 23-Nov-2015
https://dl.acm.org/doi/10.1109/TAFFC.2015.2432791
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

IR principles for content-based indexing and retrieval of functional brain images

Learning Similarity Matching in Multimedia Content-Based Retrieval

An affect-based video retrieval system with open vocabulary querying