Skip to main content
SpringerLink
Log in

Instance-Based Relevance Feedback in Image Retrieval Using Dissimilarity Spaces

  • Chapter
Case-Based Reasoning on Images and Signals

Part of the book series: Studies in Computational Intelligence ((SCI,volume 73))

Summary

High-retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. The user marks all the retrieved images as being either relevant or not, then the search engine exploits this relevance feedback to adapt the search to better meet user’s needs. The main difficulties in exploiting relevance information are (a) the gap between user perception of similarity and the similarity computed in the feature space used for the representation of image content and (b) the availability of few training data (users typically label a few dozen of images. At present, SVM are extensively used to learn from relevance feedback due to their capability of effectively tackling the above difficulties. As the performances of SVM depend on the tuning of a number of parameters, in this chapter a different approach to relevance feedback is proposed. First, images are represented in the dissimilarity space made up of the dissimilarities from the set of relevant images. Then a relevance score is computed in terms of the distance from the nearest nonrelevant image, and the distance from the nearest relevant one. Images are ranked according to this score and the top k images are displayed. Reported results show that the performances of the proposed approach are comparable to the highest performances that can be attained by SVM by suitably tuning the learning parameters.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Aha DW, Kibler D, Albert MK (1991) Instance Based learning Algorithms. Machine Learning 6:37–66

    Google Scholar 

  2. Althoff K-D (2001) Case-Based Reasoning. In Chang S.K. (ed.) Handbook on Software Engineering and Knowledge Engineering, World Scientific, 549–588

    Google Scholar 

  3. Bhanu B, Dong D (2001) Concepts Learning with Fuzzy Clustering and Relevance Feedback. In: Perner, P. (Ed.): Machine Learning and Data Mining in Pattern Recognition. LNAI 2123, Springer-Verlag, Berlin 102–116

    Chapter  Google Scholar 

  4. Breunig M, Kriegel H-P, Ng R, Sander J. (2000) LOF: indentifying density-based local outliers. In Proc. of the ACM SIGMOD 2000 Int. Conf. on management of data

    Google Scholar 

  5. Bruno E, Loccoz N, Maillet S (2005) Learning user queries in multimodal dissimilarity spaces. Proc. of the 3rd Int’l Workshop on Adaptive Multimedia Retrieval

    Google Scholar 

  6. Cox I.J, Miller ML, Minka TP, Papathomas TV, Yianilos PN (2000) The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Trans. on Image Processing 9:20–37

    Article  Google Scholar 

  7. Dasarathy DV (Ed.) (1991) Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Press

    Google Scholar 

  8. Del Bimbo A (1999) Visual Information Retrieval. Morgan Kaufmann Pub. Inc., San Francisco, CA

    Google Scholar 

  9. Duda RO, Hart PE, Stork DG (2001) Pattern Classification. John Wiley and Sons, Inc., New York

    MATH  Google Scholar 

  10. Duin RPW, de Ridder D, Tax DMJ (1997) Experiments with object based discriminant functions: a featureless approach to pattern recognition. Pattern Recognition Letters 18:1159–1166

    Article  Google Scholar 

  11. Frederix G, Caenen G, Pauwels EJ (2000) PARISS: Panoramic, Adaptive and Reconfigurable Interface for Similairty Search. Proc. of ICIP 2000 Intern. Conf. on Image Processing. WA 07.04, vol. III, 222–225

    Google Scholar 

  12. Giacinto G, Roli F (2003) Dissimilarity Representation of Images for Relevance Feedback in Content-Based Image Retrieval. In: Perner P. (Ed.) Machine Learning and Data Mining in Pattern Recognition. LNAI 2734, Springer-Verlag, Berlin 202–214

    Chapter  Google Scholar 

  13. Giacinto G, Roli F (2004) Bayesian Relevance Feedback for Content-Based Image Retrieval. Pattern Recognition 37:1499–1508

    Article  MATH  Google Scholar 

  14. Giacinto G, Roli F (2005) Instance-Based Relevance Feedback for Image Retrieval. In Saul L.K., Weiss Y., and Bottou L.: Advances in Neural Information Processing Systems 17, MIT Press 489–496

    Google Scholar 

  15. Ishikawa Y, Subramanys R, Faloutsos C (1998) MindReader: Querying databases through multiple examples. In Proceedings. of the 24th VLDB Conference 433–438

    Google Scholar 

  16. Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-Based Multimedia Information Retrieval: State of the Art and Challenges. ACM Trans. On Multimedia Computing, Communications and Applications 2:1–19

    Google Scholar 

  17. McG Squire D, Müller W, Müller H, Pun T (2000) Content-based query of image databases: inspirations from text retrieval. Pattern Recognition Letters 21:1193–1198

    Article  MATH  Google Scholar 

  18. McKenna E, Smyth B (2000) Competence-Guided Case-Base Editing Techniques in Blanzeri and Portinale (Eds.) Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning, LNCS 1898, Springer-Verlag: 186–197

    Google Scholar 

  19. Moghaddam B, Tian Q, Lesh N, Shen C, Huang TS (2004) Visualization and User-Modeling for Browsing Personal Photo Libraries. International Journal of Computer Vision 56:109–130

    Article  Google Scholar 

  20. Nguyen GP, Worring M, Smeulders AWM (2006) Similarity learning via dissimilarity space in CBIR. Proc. of the 8th ACM Int’l workshop on Multimedia Information retrieval 107–116

    Google Scholar 

  21. Ortega M, Rui Y, Chakrabarti K, Porkaew K, Mehrotra S, Huang TS (1998) Supporting ranked boolean similarity queries in MARS. IEEE Trans. on KDE 10:905–925

    Google Scholar 

  22. Pekalska E, Duin RPW (2002) Dissimilarity representations allow for building good classifiers. Pattern Recognition Letters 23:943–956

    Article  MATH  Google Scholar 

  23. Pekalska E, Duin RPW (2005) The dissimilarity representation for pattern recognition: foundations and applications. World Scientific Publishing

    Google Scholar 

  24. Pekalska E, Duin RPW, Paclick (2006) Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39:189:208

    Article  MATH  Google Scholar 

  25. Peng J, Bhanu B, Qing S (1999) Probabilistic feature relevance learning for content-based image retrieval. Computer Vision and Image Understanding 75:150–164

    Article  Google Scholar 

  26. Perner P (2002) Are case-based reasoning and dissimilarity-based classification two sides of the same coin? Engineering Applications of Artificial Intelligence 15:193–203

    Article  Google Scholar 

  27. Rui Y, Huang TS, Mehrotra S (1997) Content-based image retrieval with relevance feedback in MARS. In Proceedings of the IEEE International Conference on Image Processing, IEEE Press 815–818

    Google Scholar 

  28. Rui Y, Huang TS (2001) Relevance Feedback Techniques in Image retrieval. In Lew M.S. (ed.): Principles of Visual Information Retrieval. Springer-Verlag, London, 219–258

    Google Scholar 

  29. Salton G, McGill MJ (1998) Introduction to modern information retrieval. McGraw-Hill, New York

    Google Scholar 

  30. Santini S, Jain R (1999) Similarity Measures. IEEE Trans. on Pattern Analysis and Machine Intelligence 21:871–883

    Article  Google Scholar 

  31. Santini S, Jain R (2000) Integrated browsing and querying for image databases. IEEE Multimedia 7:26–39

    Article  Google Scholar 

  32. Sclaroff S, La Cascia M, Sethi S, Taycher L (2001) Mix and Match Features in the ImageRover search engine. In Lew M.S. (ed.): Principles of Visual Information Retrieval. Springer-Verlag, London 219–258

    Google Scholar 

  33. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis and Machine Intelligence 22:1349–1380

    Article  Google Scholar 

  34. Tao D, Tang X, Li X, Rui Y (2006) Direct Kernel Biased Discriminant Analysis: A New Content-based Image Retrieval Relevance Feedback Algorithm. IEEE Trans. on Multimedia 8:716–727

    Article  Google Scholar 

  35. Tao D, Tang X, Li X, Wu X (2006) Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 28:1088–1099

    Article  Google Scholar 

  36. Tax D (2001) One-class classification. PhD thesis, Delft University of Technology, The Netherlands

    Google Scholar 

  37. Tieu K, Viola P (2001) Boosting Image Retrieval. Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, vol. 1, 228–235

    Google Scholar 

  38. Tong S, Chang E (2001) Support Vector Machine Active Learning for Image Retrieval. Proc. ACM Int’l Conf. Multimedia 107–118

    Google Scholar 

  39. Zhang L, Lin F, Zhang B (2001) Support Vector Machine Learning for Image Retrieval. Proc. IEEE Int’l Conf. Image Processing 721–724

    Google Scholar 

  40. Zhou X, Huang TS (2001) Small Sample Learning During Multimedia Retrieval Using Biasmap. Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, vol. 1, 11–17

    Google Scholar 

  41. Zhou X, Huang TS (2003) Relevance Feedback for Image Retrieval: A Comprehensive Review, ACM Multimedia Systems 8:536–544

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Italy

    G. Giacinto & F. Roli

Authors
  1. G. Giacinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Roli
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Arno-Nitzsche-Str.43, 04277, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Giacinto, G., Roli, F. (2008). Instance-Based Relevance Feedback in Image Retrieval Using Dissimilarity Spaces. In: Perner, P. (eds) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73180-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73180-1_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73178-8

  • Online ISBN: 978-3-540-73180-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation