Mixture of KL subspaces for relevance feedback | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

Mixture of KL subspaces for relevance feedback

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches limit the utilization of the user’s feedback to a single search session, performing a short-term learning. In this paper we present a novel approach for short and long term learning, based on the definition of an adaptive similarity metric and of a high level representation of the images. For short-term learning, the relevant and non-relevant information given by the user during the feedback process is employed to create a positive and a negative subspace of the feature space. For long-term learning, the feedback history of all the users is exploited to create and update a representation of the images which is adopted for improving retrieval performance and progressively reducing the semantic gap between low-level features and high-level semantic concepts. The experimental results prove that the proposed method outperforms many other state of art methods in the short-term learning, and demonstrate the efficacy of the representation adopted for the long-term learning.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ashwin T.V., Navendu J, Sugata G (2001) Improving image retrieval performance using negative relevance feedback. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 2437–2440

  2. Cappelli R, Lumini A, Maio D. (2002) MKL-tree: a hierarchical data structure for indexing multidimensional data. In: Proc. of the International Conference on Database and Expert Systems Applications (DEXA) pp 914–924

  3. Cappelli R, Maio D, Maltoni D (2001) Multi-space KL for pattern representation and classification. IEEE Trans Pattern Anal Mach Intell 23(9):977–996

    Article  Google Scholar 

  4. Corel Gallery Magic 65000 (1999) http://www.corel.com

  5. Cox IJ, Minka TP, Papathomas TV, Yianilos PN (2000) The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Trans Image Process (special issue on digital libraries) 9(1):20–37

    Article  Google Scholar 

  6. Dieraba C (2003) Association and content-based retrieval. IEEE Trans Knowl Data Eng 15(1):118–135

    Article  Google Scholar 

  7. Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P (1995) Query by image and video content: the QBIC system. IEEE Computer 28:23–32

    Google Scholar 

  8. Franco A, Lumini A, Maio D (2004) A new approach for relevance feedback through positive and negative samples. In: Proceedings International Conference on Pattern Recognition (ICPR04), Cambridge (United Kingdom), vol 4, pp 905–908

  9. Franco A, Lumini A, Maio D (2002) Eigenspace merging for model updating. In: Proc. of the International Conference on Pattern Recognition (ICPR), vol 2, pp 156–159

  10. Fukunaga K (1990) Statistical pattern recognition. Academic, San Diego, CA

    MATH  Google Scholar 

  11. Corel Features: http://kdd.ics.uci.edu/databases/CorelFeatures/CorelFeatures.data.html

  12. Vision Research Lab: http://vision.ece.ucsb.edu/texture/feature.html

  13. IMSI MasterPhotos 50,000: http://www.imsisoft.com

  14. Ishikawa Y, Subramanya R, Faloutsos C (1998) MindReader: query databases through multiple examples In: Proc. of the Int. Conf. on Very Large Databases, pp 218–227

  15. Jardine N, Van Rijsbergen CJ (1971) The use of hierarchical clustering in information retrieval. Inf Storage Retr 7:217–240

    Article  Google Scholar 

  16. Kherfi ML, Ziou D., Bernardi A (2002) Learning from negative example in relevance feedback for content-based image retrieval. In: Proc. of the International Conference on Pattern Recognition (ICPR), pp 933–936

  17. Kim DH, Chung CW (2003) Qcluster: relevance feedback using adaptive clustering for content-based image retrieval. In: Proc. SIGMOD International Conference on Management of Data, pp 599–610

  18. Koskela M, Laaksonen J (2003) Using long-term learning to improve efficiency of content-based image retrieval. In: Proc. Pattern Recognition in Information Systems (PRIS), pp 72–79

  19. Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. In: ACM Transactions on Multimedia Computing, Communications, and Applications

  20. Li M, Chen Z, Zhang H (2002) Statistical correlation analysis in image retrieval. Pattern Recogn 35(12):2687–2693

    Article  MATH  MathSciNet  Google Scholar 

  21. Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

    Article  Google Scholar 

  22. Mongy S, Bouali F, Djeraba C (2005) Analyzing user’s behavior on a video database. In: Proceedings of ACM MDM/KDD Workshop on Multimedia Data Mining

  23. Muller H, Muller W, Squire D (2000) Learning feature weights from user behavior in content-based image retrieval In: Proc. of the International Workshop on Multimedia Data Mining, pp 67–72

  24. Nakazato M, Dagli C, Huang TS (2003) Evaluating group-based relevance feedback for content-based image retrieval. In: Proc. International Conference on Image Processing, pp 599–602

  25. Nastar C, Mitschke M, Meihac C (1998) Efficient query refinement for image retrieval. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp 547–552

  26. Peng J (2003) Multi-class relevance feedback content-based image retrieval. Comput Vis Image Underst 90(1):42–67

    Article  Google Scholar 

  27. Porkaew K, Chakrabarti K (1999) Query refinement for multimedia similarity retrieval in MARS. In: Proc. of the ACM Multimedia Conference, pp 235–238

  28. Rocchio J (1971) Relevance feedback information retrieval. In Salton G (ed) The Smart retrieval system experiments in automatic document proc. Prentice-Hall, Englewood, NJ

    Google Scholar 

  29. Rui Y, Huang TS (2000) Optimizing learning in image retrieval. In: Proc. of the Conf on Computer Vision and Pattern Recognition (CVPR), pp 236–245

  30. Rui Y, Huang TS, Mehrotra S (1997) Content-based image retrieval with relevance feedback in MARS. In: Proc. IEEE International Conference on Image Processing, pp 68–75

  31. Rui Y, Huang TS, Mehrotra S, Ortega M (1997) A relevance feedback architecture for content-based multimedia information retrieval systems. In: Proc. of the IEEE Workshop Content-based Access of Image and Video Libraries, pp 82–89

  32. Su Z, Zhang H, Li SZ, Ma S (2003) Relevance feedback in content-based image retrieval: Bayesian framework, feature subspace and progressive learning. IEEE Trans Image Process 12(8):253–262

    Google Scholar 

  33. Tieu K, Viola P (2004) Boosting image retrieval. Int J Comput Vis 56(1):17–36

    Article  Google Scholar 

  34. Vasconcelos N, Lippman A (1999) Learning from User Feedback in Image Retrieval Systems. In: Proc. Neural Information Processing Systems (NIPS) pp 977–986

  35. Yin P, Bhanu B, Chang K, Dong A (2002) Improving retrieval performance by long-term relevance information. In: Proc. of the International Conference on Pattern Recognition, pp 533–536

  36. Zhou XS, Huang TS (2003) Relevance feedback for image retrieval: a comprehensive review. Multimedia Syst 8(6):536–544

    Article  Google Scholar 

  37. Zhou XS, Huang TS (2001) Comparing discriminate transformations and SVM for learning during multimedia retrieval. In: Proc. ACM International Conference on Multimedia, pp 137–146

Download references

Author information

Authors and Affiliations

  1. Corso di Laurea in Scienze dell’Informazione, Università di Bologna, via Sacchi 3, 47023, Cesena, Italy

    Annalisa Franco & Alessandra Lumini

Authors
  1. Annalisa Franco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessandra Lumini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Annalisa Franco.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Franco, A., Lumini, A. Mixture of KL subspaces for relevance feedback. Multimed Tools Appl 37, 189–209 (2008). https://doi.org/10.1007/s11042-007-0139-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-007-0139-2

Keywords

Search

Navigation