Skip to main content
Log in

Interactive image retrieval using M-band wavelet, earth mover’s distance and fuzzy relevance feedback

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

We propose an interactive content based image retrieval (CBIR) system using M-band wavelet features with earth mover’s distance (EMD). A fuzzy relevance feedback (FRF) method is proposed to enhance the retrieval mechanism in order to retrieve more images which are semantically close to the query. M × M sub-bands coefficient are used as primitive features, on which, for each pixel, energies are computed over a neighborhood and are taken as features for each pixel to characterize its color and texture properties. Based on the energy property, pixels are clustered using fuzzy C-means algorithm to obtain an image signature. The EMD is used as a distance measure between the signatures for different images of the database. Combining information both from relevant and irrelevant images marked by the user, fuzzy entropy based feature evaluation mechanism is used for automatic computation of revised feature importance and similarity distance at the end of each iteration. The proposed CBIR system performance using M-band wavelets feature are compared to that of Moving Picture Expert Group-7 visual features which have almost become a standard benchmark for both video and image representation and comparison. The proposed FRF technique using EMD is compared with different other similarity measures to test the effectiveness of the system on standard image database.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Chen Y, Wang JZ, Krovet R (2005) Cluster based retrieval of images by unsupervised learning. IEEE Trans Image Process 14:1187–1201

    Article  Google Scholar 

  2. Kekre HB, Thepade SD, Maloo A (2010) Image retrieval using fractional coefficients of transformed image using DCT and Walsh transform. Int J Eng Sci Technol 2:362–371

    Google Scholar 

  3. Heesch D (2008) A survey of browsing models for content based image retrieval. Multimed Tools Appl 40:1380–7501

    Article  Google Scholar 

  4. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content based image retrieval at the end of early years. IEEE Trans Pattern Anal Mach Intell 22:1349–1380

    Article  Google Scholar 

  5. Xiaoling W, Hongyan M (2009) Enhancing color histogram for image retrieval. In: Proceedings of the international workshop on information security and application. Academy Publisher, New York

  6. Ksantini R, Ziou D, Dubeau F (2006) Image retrieval based on region separation and multiresolution analysis. Int J Wavelets Multiresolution Inf Process 4:147–175

    Article  MathSciNet  MATH  Google Scholar 

  7. Banerjee M, Kundu MK, Maji P (2009) Content based image retrieval using visually significant point features. Fuzzy Sets Syst 160:3323–3341

    Article  MathSciNet  Google Scholar 

  8. Santini S, Jain R (1999) Similarity measures. IEEE Trans Pattern Anal Mach Intell 21:871–883

    Article  Google Scholar 

  9. Rubner Y, Tomasi C (2001) Perceptual metrics for image database navigation. Kluwer, Dordrecht

    MATH  Google Scholar 

  10. Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40:99–121

    Article  MATH  Google Scholar 

  11. Yin P-Y, Bhanu B, Chang K-C, Dong A (2005) Integrating relevance feedback techniques for image retrieval using reinforcement learning. IEEE Trans Pattern Anal Mach Intell 27:1536–1551

    Article  Google Scholar 

  12. Han J, Ngan KN, Li M, Zhang HJ (2005) A memory learning framework for effective image retrieval. IEEE Trans Image Process 14:521–524

    Article  Google Scholar 

  13. Chang FC, Hang HM (2006) A relevance feedback image retrieval scheme using multi-instance and pseudo image concepts. IEICE Trans Inf Syst 89-D(5):1720–1731

    Article  Google Scholar 

  14. Kim W-C, Song J-Y, Kim S-W, Park S (2008) Image retrieval model based on weighted visual features determined by relevance feedback. Inf Sci 178:4301–4313

    Article  Google Scholar 

  15. Rui Y, Huang TS, Ortega M, Mehrotra S (1997) Content-based image retrieval with relevance feedback in mars. Proc IEEE Int Conf Image Process 2:815–818

    Article  Google Scholar 

  16. Jin Z, King I, Li XQ (2000) Content-based image retrieval by relevance feedback. In: Advances in visual information systems. Lecturer notes in computer science, vol 1929. Springer, Berlin, pp 639–648

  17. Marakakis A, Galatsanos N, Likas A, Stafylopatis A (2009) Probabilistic relevance feedback approach for content-based image retrieval based on Gaussian mixture models. IET Image Process 3:10–15

    Article  Google Scholar 

  18. Ves ED, Domingo J, Ayala G, Zuccarello P (2006) A novel Bayesian framework for relevance feedback in image content-based retrieval systems. Pattern Recognit 39:1622–1632

    Article  MATH  Google Scholar 

  19. Shi Z, He Q, Shi Z (2007) Bayes-based relevance feedback method for CBIR. Adv Soft Comput 44:264–271

    Article  Google Scholar 

  20. Qian F, Zhang B, Lin F (2003) Constructive learning algorithm-based RBF network for relevance feedback in image retrieval. In: Proceedings of the 2nd international conference on Image and video retrieval. Lecturer notes in computer science, vol 2728. Springer, Berlin, pp 352–361

  21. He X, King O, Ma W, Li M, Zhang HJ (2003) Learning a semantic space from user’s relevance feedback for image retrieval. IEEE Trans Circuits Syst Video Technol 13:39–48

    Article  Google Scholar 

  22. Acharyya M, Kundu MK (2008) Extraction of noise tolerant, gray-scale transform and rotation invariant features for texture segmentation using wavelet frames. Int J Wavelets Multiresolution Inf Process 6:391–417

    Article  MathSciNet  MATH  Google Scholar 

  23. Manjunath BS, Ohm JR, Vasudevan VV (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11:703–715

    Article  Google Scholar 

  24. Wu P, Manjunath BS, Newsam S, Shin HD (2000) A texture descriptor for browsing and similarity retrieval. Signal Process Image Commun 16:33–43

    Article  Google Scholar 

  25. Manjunath BS, Salembier P, Sikora T (2002) Introduction to MPEG-7: multimedia content description interface. Wiley, New York

    Google Scholar 

  26. Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23:947–963

    Article  Google Scholar 

  27. Acharyya M, Kundu MK (2001) An adaptive approach to unsupervised texture segmentation using M-band wavelet transform. Signal Process 81:1337–1356

    Article  MATH  Google Scholar 

  28. Burrus CS, Gopinath A, Guo H (1998) Introduction to wavelets and wavelet transform: a primer. Prentice Hall International Editions, Englewood Cliffs

    Google Scholar 

  29. Kundu MK, Banerjee M, Bagrecha P (2009) An interactive image retrieval in a fuzzy framework. In: Proceedings 8th international workshop on fuzzy logic and application. Lecturer notes in artificial intelligence, vol 5571. Springer, Berlin, pp 246–253

  30. Kundu MK, Acharyya M (2003) M-band wavelet: application to texture segmentation for real life image analysis. Int J Wavelets Multiresolution Inf Process 1:115–149

    Article  MATH  Google Scholar 

  31. Acharyya M, De RK, Kundu MK (2003) Extraction of features using M-band wavelet packet frames and their neuro-fuzzy evaluation for multi-texture segmentation. IEEE Trans Pattern Anal Mach Intell 25:1639–1644

    Article  Google Scholar 

  32. Pal SK, Majumder DD (1985) Fuzzy mathematical approach to pattern recognition. Wiley Eastern Limited, New York

    Google Scholar 

  33. Liang J, Song W (2011) Clustering based on Steiner points. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0047-7

  34. Graaff AJ, Engelbrecht AP (2011) Clustering data in stationary environments with a local network neighborhood artificial immune system. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0041-0

  35. Guo G, Chen S, Chen L (2011) Soft subspace clustering with an improved feature weight self-adjustment mechanism. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0038-8

  36. Wang XZ, Zhai JH, Lu SX (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178:3188–3202

    Article  MathSciNet  MATH  Google Scholar 

  37. Wang XZ, Dong CR (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17:556–567

    Article  Google Scholar 

  38. Jun W, Shitong W, Chung FL (2011) Positive and negative fuzzy rule system, extreme learning machine and image classification. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0024-1

  39. Liu H, Song D, Rüger S, Hu R, Uren V (2008) Comparing dissimilarity measures for content-based image retrieval. In: The 4th Asia information retrieval symposium (AIRS2008). Springer, Berlin, pp 44-50

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manish Chowdhury.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kundu, M.K., Chowdhury, M. & Banerjee, M. Interactive image retrieval using M-band wavelet, earth mover’s distance and fuzzy relevance feedback. Int. J. Mach. Learn. & Cyber. 3, 285–296 (2012). https://doi.org/10.1007/s13042-011-0062-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-011-0062-8

Keywords

Navigation