Multimedia Tools and Applications

, Volume 78, Issue 2, pp 1889–1911 | Cite as

A novel technique for content based image retrieval based on region-weight assignment

  • Ghanshyam Raghuwanshi
  • Vipin TyagiEmail author


This paper presents a novel technique for content based image retrieval (CBIR) that selects and assigns weights to the regions of the image on the basis of their contribution to image contents, using a new region-weight assignment scheme. Assigning the weight to each region ignores the irrelevant regions of the image during retrieval and thus maximizes the retrieval accuracy. The proposed approach performs the feature extraction at both region-level and image-level. Texture and edge features are extracted at region-level whereas shape feature is extracted at image-level. At region-level, the image is divided into non-overlapping regions and texture and edge features are calculated for each region separately. Curvelet transform is used for extracting the texture feature using the curve continuity as well as line continuity in the feature extraction process. Moment invariant is used for extracting the shape features. Integrated Region Matching (IRM) technique is used for retrieving the relevant images. The proposed approach does the best use of the features by balancing the regions and features in the similarity matching of the regions. The performance of the proposed technique is tested on COREL and CIFAR databases. Experimental results show the effectiveness of proposed region weight assignment scheme over the feature weight assignment scheme in image retrieval in comparison to other state-of-the-art techniques.


Content based image retrieval Curvelet transform Image search Region-based image retrieval Region weight assignment 



  1. 1.
    Candes EJ, Donoho DL (1999) “Curvelets- a surprisingly effective non adaptive representation for objects with edges”, curve and surface fitting: Saint-Malo. Vanderbilt University Press, NashvilleGoogle Scholar
  2. 2.
    Candes EJ, Donoho DL (1999) Ridglets: a key to higher-dimensional intermittency? Philos Trans R Soc Lond 357:2495–2509CrossRefGoogle Scholar
  3. 3.
    Candes EJ, Demanet L, Donoho DL, Ying L (2005) Fast discrete curvelet transforms. Multiscal Model Simul 5:861–899MathSciNetCrossRefGoogle Scholar
  4. 4.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698CrossRefGoogle Scholar
  5. 5.
    ElAlami ME (2011) A novel image retrieval model based on the most relevant features. Knowl-Based Syst 24(1):23–32CrossRefGoogle Scholar
  6. 6.
    Fadaei S, Amirfattahi R, Ahmadzadeh MR (2017) New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features. IET Image Process 11(2):89–98CrossRefGoogle Scholar
  7. 7.
    Feng D, Siu WC, Zhang HJ (2003) Fundamentals of content-based image retrieval, in multimedia information retrieval and management—technological fundamentals and applications. Springer, New York, pp 1–26Google Scholar
  8. 8.
    Gonde AB, Maheshwari RP, Balasubramanian R (2013) Modified curvelet transform with vocabulary tree for content based image retrieval. Dig Sig Proc 23(1):142–150MathSciNetCrossRefGoogle Scholar
  9. 9.
    Guo JM, Prasetyo H, Farfoura ME, Lee H (2015) Vehicle verification using features from Curvelet transform and generalized Gaussian distribution modeling. IEEE Trans Intell Transp Syst 16(4)Google Scholar
  10. 10.
    J Harel, C Koch, P Perona (2006) Graph-Based Visual Saliency. Proc Neu Info Proc Syst (NIPS). 545–552Google Scholar
  11. 11.
    Hu MK (1962) Visual pattern recognition by moment invariants. IEEE Trans Inf Theory 12:179–187zbMATHGoogle Scholar
  12. 12.
    Huang PW, Dai SK (2003) Image retrieval by texture similarity. Pattern Recogn 36(3):665–679CrossRefGoogle Scholar
  13. 13.
    Itti L, Koch C (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vis Res 40:1489–1506CrossRefGoogle Scholar
  14. 14.
    Jacob J, Srinivasagan KG, Jayapriya K (2014) Local oppugnant color texture pattern for image retrieval system. Pattern Recogn Lett 42(1):72–88CrossRefGoogle Scholar
  15. 15.
    Jhanwar N, Chaudhuri S, Seetharaman G, Zavidovique B (2004) Content based image retrieval using motif co-occurrence matrix. Image Vis Comput 22(14):1211–1220CrossRefGoogle Scholar
  16. 16.
    Kimura M, Yamauchi M (2006) A method for extracting region of interest based on attractiveness. IEEE Trans Consum Electron 52(2):312–316CrossRefGoogle Scholar
  17. 17.
    Kingsbury NG (1999) Image processing with complex wavelets. Philosoph Trans R Soc B Biol Sci 357:2543–2560. CrossRefzbMATHGoogle Scholar
  18. 18.
    Kokare M, Biswas PK, Chatterji BN (2005) Texture image retrieval using new rotated complex wavelet filters. IEEE Trans Cybernet 35(6):1168–1178CrossRefGoogle Scholar
  19. 19.
    Kumar KM, C M, Bulo SR (2015) A graph-based relevance feedback mechanism in content-based image retrieval. Knowl-Based Syst 73:254–264CrossRefGoogle Scholar
  20. 20.
    Kwitt R, Meerwald P, Uhl A (2011) Efficient texture image retrieval using copulas in a bayesian framework. IEEE Trans Image Process 20(7)Google Scholar
  21. 21.
    Lai C-C, Chen Y-C (2011) A user-oriented image retrieval system based on interactive genetic algorithm. IEEE Trans Instrum Meas 60(10):3318–3325CrossRefGoogle Scholar
  22. 22.
    Lin C-H, Chen R-T, Chan Y-K (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27(6):658–665CrossRefGoogle Scholar
  23. 23.
    Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842CrossRefGoogle Scholar
  24. 24.
    Mosbah M, Boucheham B (2014) Relevance feedback within CBIR systems. Int J Comput Electric, Auto, Control Info Eng 8(4)Google Scholar
  25. 25.
    Murala S, Maheshwari RP, Balasubramanian R (2012) Directional local Extrema patterns: a new descriptor for content based image retrieval. Int J Multimed Info Retriev 1(3):191–203CrossRefGoogle Scholar
  26. 26.
    Raghuwanshi G, Tyagi V (2016) Texture image retrieval using adaptive tetrolet transforms. Dig Sig Proc 48:50–57MathSciNetCrossRefGoogle Scholar
  27. 27.
    Raghuwanshi G, Tyagi V (2017) Novel technique for location independent object based image retrieval. Multimed Tools Appl 76(12):13741–13759CrossRefGoogle Scholar
  28. 28.
    Raghuwanshi G, Tyagi V (2018) Feed-forward content based image retrieval using adaptive tetrolet transforms. Multimed Tools Appl.
  29. 29.
    Reddy AH, Chandra NS (2015) Local oppugnant color space Extrema patterns for content based natural and texture image retrieval. Int J Electron Comm (AEÜ) 69(1):290–298CrossRefGoogle Scholar
  30. 30.
    Reddy PVB, Reddy ARM (2014) Content based image indexing and retrieval using directional local Extrema and magnitude patterns. Int J Electron Comm (AEÜ) 68(7):637–643CrossRefGoogle Scholar
  31. 31.
    F Shen, C Shen, W Liu, HT Shen (2015) Supervised discrete hashing. Proc IEEE Conf Comput Vis Patt Recog 37–45Google Scholar
  32. 32.
    Shen F, Zhou X, Yang Y, Song J, Shen HT, Tao D (2016) A fast optimization method for general binary code learning. IEEE Trans Image Process 25(12):5610–5621MathSciNetCrossRefGoogle Scholar
  33. 33.
    Shrivastava N, Tyagi V (2014) A review of ROI image retrieval techniques. Adv Intel Syst Computing 328:509–520CrossRefGoogle Scholar
  34. 34.
    Shrivastava N, Tyagi V (2014) Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Inf Sci 259:212–224CrossRefGoogle Scholar
  35. 35.
    Shrivastava N, Tyagi V (2014) An efficient technique for retrieval of color images in large databases. Comput Electr Eng 16:314–327Google Scholar
  36. 36.
    IJ Sumana, MM Islam, D Zhang, G Lu (2008) Content based image retrieval using curvelet transform. 10th Workshop IEEE Multimed Sig Proc Cairns Qld 11–16Google Scholar
  37. 37.
    Yildizer E, Balci AM, Jarada TN, Alhajj R (2012) Integrating wavelets with clustering and indexing for effective content-based image retrieval. Knowl-Based Syst 31:55–66CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Jaypee University of Engineering and TechnologyGunaIndia

Personalised recommendations