Skip to main content
SpringerLink
Log in

Interactive Image Retrieval Based on Relevance Feedback

  • Chapter
Book cover Multimodal Interaction in Image and Video Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 48))

Abstract

This chapter presents a prototype of a web image search engine that implements four approaches to improve the performance of interactive image retrieval systems. The first approach is classic relevance feedback, which relies on user feedback to provide better retrievals in an iterative process. It adopts a probabilistic model which leads to maximizing the relevance of the images retrieved. The second approach is based on user relevance feedback as well, but the attention is focused on combining several information sources to the retrieval mechanism. In particular, we propose a retrieval technique that combines both visual and textual features using dynamic late fusion. The third and fourth approaches are query refinement and tag cloud, both consisting of leveraging the information derived from the relevance feedback and the (textual) image annotations. In the former, a refinement of the initial textual query is suggested. In the latter, a tag cloud is given to provide an overall topic formation related to the user’s image selection.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bateman, S., Gutwin, C., Nacenta, M.: Seeing things in the clouds: the effect of visual features on tag cloud selections. In: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia (HT), pp. 193–202 (2008)

    Google Scholar 

  2. La Cascia, M., Sethi, S., Sclaroff, S.: Combining textual and visual cues for content-based image retrieval on the world wide web. In: Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL), pp. 24–28 (1998)

    Google Scholar 

  3. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)

    Article  Google Scholar 

  4. Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: An experimental comparison. Information Retrieval 11(2), 77–107 (2008)

    Article  Google Scholar 

  5. Duda, R., Hart, P.: Pattern Recognition and Scene Analisys. John Wiley, New York (1973)

    Google Scholar 

  6. Faloutsos, C., Barber, R., Flickner, M., Hafner, J., Niblack, W., Petkovic, D., Equitz, W.: Efficient and effective querying by image content. Journal of Intelligent Information Systems 3(3/4), 231–262 (1994)

    Article  Google Scholar 

  7. Giacinto, G., Rolli, F.: Instance-based relevance feedback for image retrieval. In: Neural Information Processing Systems, NIPS (2004)

    Google Scholar 

  8. Halvey, M.J., Keane, M.T.: An assessment of tag presentation techniques. In: Proceedings of the 16th International Conference on World Wide Web (WWW), pp. 1313–1314 (2007)

    Google Scholar 

  9. Hiemstra, D.: A probabilistic justification for using tf×idf term weighting in information retrieval. Intrenational Journal of Digital Libraries 3(1), 131–139 (2000)

    Article  Google Scholar 

  10. Jin, H., Tao, W., Sun, A.: Vast: Automatically combining keywords and visual features for web image retrieval. In: International Conference on Advanced Communication Technology (ICACT), pp. 2188–2193 (2008)

    Google Scholar 

  11. Leiva, L.A., Villegas, M., Paredes, R.: Query refinement suggestion in multimodal interactive image retrieval. In: Proceedings of the 13th International Conference on Multimodal Interaction (ICMI), pp. 311–314 (2011)

    Google Scholar 

  12. Moran, S.: Automatic image tagging. Master’s thesis, School of Informatics, University of Edinburgh (2009)

    Google Scholar 

  13. Paredes, R., Deselaers, T., Vidal, E.: A Probabilistic Model for User Relevance Feedback on Image Retrieval. In: Popescu-Belis, A., Stiefelhagen, R. (eds.) MLMI 2008. LNCS, vol. 5237, pp. 260–271. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. Pham, T.-T., Maillot, N.E., Lim, J.-H., Chevallet, J.-P.: Latent semantic fusion model for image retrieval and annotation. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management (CIKM), pp. 439–444 (2007)

    Google Scholar 

  15. Rivadeneira, A.W., Gruen, D.M., Muller, M.J., Millen, D.R.: Getting our head in the clouds: toward evaluation studies of tagclouds. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 995–998 (2007)

    Google Scholar 

  16. Robertson, S.E., Sparck-Jones, K.: Relevance weighting of search terms. Journal of the American Society for Informatn Sciences 27(3), 129–146 (1976)

    Article  Google Scholar 

  17. Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall (1971)

    Google Scholar 

  18. Setia, L., Ick, J., Burkhardt, H.: Svm-based relevance feedback in image retrieval using invariant feature histograms. In: IAPR Workshop on Machine Vision Applications (MVA), pp. 542–545 (2005)

    Google Scholar 

  19. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. PAMI 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  20. Smith, J.R., Chang, S.-F.: Tools and techniques for color image retrieval. In: SPIE Storage and Retrieval for Image and Video Databases, pp. 426–437 (1996)

    Google Scholar 

  21. Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7(1), 11–32 (1991)

    Article  Google Scholar 

  22. Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transaction on Systems, Man, and Cybernetics 8(6), 460–472 (1978)

    Article  Google Scholar 

  23. Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 1958–1970 (2008)

    Article  Google Scholar 

  24. Toselli, A.H., Vidal, E., Casacuberta, F. (eds.): Multimodal Interactive Pattern Recognition and Applications, 1st edn. Springer (2011)

    Google Scholar 

  25. Vasconcelos, N., Lippman, A.: Bayesian modeling of video editing and structure: Semantic features for video summarization and browsing. In: ICIP, pp. 153–157 (1998)

    Google Scholar 

  26. Vidal, E., Rodríguez, L., Casacuberta, F., García-Varea, I.: Interactive pattern recognition. In: Proceedings of the 4th International Conference on Machine Learning for Multimodal Interaction (MLMI), pp. 60–71 (2008)

    Google Scholar 

  27. Villegas, M., Paredes, R.: Image-text dataset generation for image annotation and retrieval. In: II Congreso Español de Recuperación de Información, CERI 2012, pp. 115–120 (2012)

    Google Scholar 

  28. Wang, J.Z., Boujemaa, N., Del Bimbo, A., Geman, D., Hauptmann, A.G., Tešić, J.: Diversity in multimedia information retrieval research. In: Proc. MIR, pp. 5–12 (2006)

    Google Scholar 

  29. Chi Wong, H., Bern, M., Goldberg, D.: An image signature for any kind of image. In: Proc. of International Conference on Image Processing, pp. 409–412 (2002)

    Google Scholar 

  30. Yu, C.T., Salton, G.: Precision weighting—an effective automatic indexing method. Journal of the ACM 23(1), 76–88 (1976)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ITI-PRHLT, Universitat Politècnica de València, València, Spain

    Mauricio Villegas, Luis A. Leiva & Roberto Paredes

Authors
  1. Mauricio Villegas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis A. Leiva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberto Paredes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mauricio Villegas .

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Villegas, M., Leiva, L.A., Paredes, R. (2013). Interactive Image Retrieval Based on Relevance Feedback. In: Multimodal Interaction in Image and Video Applications. Intelligent Systems Reference Library, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35932-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35932-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35931-6

  • Online ISBN: 978-3-642-35932-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation