Skip to main content

A Four-Factor User Interaction Model for Content-Based Image Retrieval

  • Conference paper
Advances in Information Retrieval Theory (ICTIR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5766))

Included in the following conference series:


In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Campbell, I.: Interactive evaluation of the ostensive model using a new test collection of images with multiple relevance assessments. Journal of Information Retrieval 2(1) (2000)

    Google Scholar 

  2. Dunlop, M.D.: The effect of accessing nonmatching documents on relevance feedback. ACM Transactions on Information Systems (TOIS) 15(2), 137–153 (1997)

    Article  Google Scholar 

  3. Grubinger, M., Clough, P., Müller, H., Deselaers, T.: The iapr tc-12 benchmark: A new evaluation resource for visual information systems. In: Proceedings of International Workshop OntoImage 2006 Language Resources for Content-Based Image Retrieval, pp. 13–23 (2006)

    Google Scholar 

  4. Müller, H., Müller, W., Marchand-Maillet, S., Pun, T.: Strategies for positive and negative relevance feedback in image retrieval. In: Proceedings of the International Conference on Pattern Recognition (ICPR 2000), Barcelona, Spain, September 2000, vol. 1, pp. 1043–1046 (2000)

    Google Scholar 

  5. Pickering, M.J., Rüger, S.: Evaluation of key frame-based retrieval techniques for video. Computer Vision and Image Understanding 92(2-3), 217–235 (2003)

    Article  Google Scholar 

  6. Ruthven, I., Lalmas, M., van Rijsbergen, K.: Incorporating user search behaviour into relevance feedback. Journal of the American Society for Information Science and Technology 54(6), 528–548 (2003)

    Article  Google Scholar 

  7. Saracevic, T.: Relevance reconsidered. In: Proceedings of the Second Conference on Conceptions of Library and Information Science (CoLIS 2), Copenhagen,Denmark, October 1996, pp. 210–218 (1996)

    Google Scholar 

  8. Spink, A., Greisdorf, H., Bateman, J.: From highly relevant to not relevant: examining different regions of relevance. Information Processing Management 34(5), 599–621 (1998)

    Article  Google Scholar 

  9. Urban, J., Jose, J.M., van Rijsbergen, K.: An adaptive technique for content-based image retrieval. Multimedia Tools and Applications 31, 1–28 (2006)

    Article  Google Scholar 

  10. Zhou, X.S., Huang, T.S.: Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems 8(6), 536–544 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, H., Uren, V., Song, D., Rüger, S. (2009). A Four-Factor User Interaction Model for Content-Based Image Retrieval. In: Azzopardi, L., et al. Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04416-8

  • Online ISBN: 978-3-642-04417-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics