Skip to main content
Springer Nature Link
Log in

Age-Group Classification for Family Members Using Multi-Layered Bayesian Classifier with Gaussian Mixture Model

  • Conference paper
  • First Online:
Multimedia and Ubiquitous Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 240))

Abstract

This paper proposes a TV viewer age-group classification method for family members based on TV watching history. User profiling based on watching history is very complex and difficult to achieve. To overcome these difficulties, we propose a probabilistic approach that models TV watching history with a Gaussian mixture model (GMM) and implements a feature-selection method that identifies useful features for classifying the appropriate age-group class. Then, to improve the accuracy of age-group classification, a multi-layered Bayesian classifier is applied for demographic analysis. Extensive experiments showed that our multi-layered classifier with GMM is valid. The accuracy of classification was improved when certain features were singled out and demographic properties were applied.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 259.00
Price excludes VAT (USA)
Softcover Book
USD 329.99
Price excludes VAT (USA)
Hardcover Book
USD 329.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Spangler WE, Gal-Or M, May JH (2003) Using data mining to profile TV viewers. Commun ACM Mob Comput Oppor Chall 46(12):66–72

    Google Scholar 

  2. Lee S, Park S, Hong J, Yi C, Jeong S (2012) Inference for the preference of program genre using audience measurement information. In: International conference on information and knowledge, engineering, pp 224–225

    Google Scholar 

  3. Wonneberger A, Schoenbach K, Meurs LV (2009) Dynamics of individual television viewing behavior: models, empirical evidence, and a research program. Commun Stud 60(3):235–252

    Article  Google Scholar 

  4. Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396

    Google Scholar 

  5. GMMBayes Toolbox, Gaussian mixture model learning and Bayesian classification. http://www.it.lut.fi/project/gmmbayes/

Download references

Acknowledgments

This work was supported by the Electronics and Telecommunications Research Institute (ETRI) R&D Program of Korea Communications Commission (KCC), Korea [11921-03001, “Development of Beyond Smart TV Technology”].

Author information

Authors and Affiliations

  1. Research Institute of Electrical and Computer Engineering, Hanyang University, Seoul, Korea

    Chuho Yi

  2. Department of Information and Communication Engineering, Hanyang Cyber University, Seoul, Korea

    Seungdo Jeong

  3. Electronics and Telecommunications Research Institute, Smart TV Service Research Team, Daejeon, Korea

    Kyeong-Soo Han & Hankyu Lee

Authors
  1. Chuho Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seungdo Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kyeong-Soo Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hankyu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seungdo Jeong .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. Dept of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR

    Joseph Kee-Yin Ng

  3. Humanitas College, Kyung Hee University, Seoul, Korea, Republic of (South Korea)

    Hwa-Young Jeong

  4. School of Computer Science and Software Engineering, Monash University, Clayton, Victoria, Australia

    Borgy Waluyo

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht(Outside the USA)

About this paper

Cite this paper

Yi, C., Jeong, S., Han, KS., Lee, H. (2013). Age-Group Classification for Family Members Using Multi-Layered Bayesian Classifier with Gaussian Mixture Model. In: Park, J., Ng, JY., Jeong, HY., Waluyo, B. (eds) Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 240. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6738-6_142

Download citation

  • DOI: https://doi.org/10.1007/978-94-007-6738-6_142

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-6737-9

  • Online ISBN: 978-94-007-6738-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation