Multimedia Tools and Applications

, Volume 74, Issue 4, pp 1469–1488 | Cite as

K-means based histogram using multiresolution feature vectors for color texture database retrieval

  • Cong BaiEmail author
  • Jinglin Zhang
  • Zhi Liu
  • Wan-Lei Zhao


Colorand texture are two important features in content-based image retrieval. It has been shown that using the combination of both could provide better performance. In this paper, a K-means based histogram (KBH) using the combination of color and texture features for image retrieval is proposed. Multiresolution feature vectors representing color and texture features are directly generated from the coefficients of Discrete Wavelet Transform (DWT), and K-means is exploited to partition the vector space with the objective to reduce the number of histogram bins. Thereafter, a fusion of z-score normalized Chi-Square distance between KBHs is employed as the similarity measure. Experiments have been conducted on four natural color texture data sets to examine the sensitivity of KBH to its parameters. The performance of the proposed approach has been compared with state-of-the-art approaches. Results evaluated in terms of Precision-Recall and Average Retrieval Rate (ARR) show that our approach outperforms the referred approaches


Color texture retrieval K-means Discrete wavelet transform (DWT) Z-score normalization 



This work was supported by National Natural Science Foundation of China under Grant No. 61171144, the Key (Key grant) Project of Chinese Ministry of Education (No. 212053), and the Innovation Program of Shanghai Municipal Education Commission (No. 12ZZ086)


  1. 1.
    Alajlan N, Kamel M S, Freeman G H (2008) Geometry-based image retrieval in binary image databases. IEEE Trans Pattern Anal Machine Intell 30(6):1003–1013CrossRefGoogle Scholar
  2. 2.
    Alvarez S, Salvatella A, Vanrell M, Otazu X (2012) Low-dimensional and comprehensive color texture description. Comp Vision Image Underst 116(1):54–67CrossRefGoogle Scholar
  3. 3.
    Bai C, Zou W, Kpalma K, Ronsin J (2012) Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain. Electron Lett 48(23):1463–1465CrossRefGoogle Scholar
  4. 4.
    Burghouts G J, Geusebroek J M (2009) Material-specific adaptation of color invariant features. Pattern Recogn Lett 30(3):306–313CrossRefGoogle Scholar
  5. 5.
    Chen T, Ma KK, Chen LH (1998) Discrete wavelet frame representations of color texture features for image query. In: IEEE second workshop on multimedia signal processingGoogle Scholar
  6. 6.
    Chun Y D, Seo S Y, Kim N C (2003) Image retrieval using BDIP and BVLC moments. IEEE Trans Circ Systmes Video Technol 13(9):951–957CrossRefGoogle Scholar
  7. 7.
    Chun YD, Kim NC, Jang IH (2008) Content-based image retrieval using multiresolution color and texture features. IEEE Trans Multimed 10(6):1073 –1084CrossRefGoogle Scholar
  8. 8.
    Costantini L, Sit P, Capodiferro L, Neri A (2010) Laguerre gauss analysis for image retrieval based on color texture. In: Proc. SPIE, vol 7535Google Scholar
  9. 9.
    Datta R, Joshi D, Li J, Wang J Z (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60CrossRefGoogle Scholar
  10. 10.
    Do M, Vetterli M (2002) Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance. IEEE Trans Image Process 11(2):146 –158CrossRefMathSciNetGoogle Scholar
  11. 11.
    Google (2012) Google image search
  12. 12.
    Jain A K (2010) Data clustering 50 years beyond k-means. Pattern Recog Lett 31(8):651–666CrossRefGoogle Scholar
  13. 13.
    James SW (2007) A Primer on Wavelets and their scientific Appliactions Second edition. Chapman HallGoogle Scholar
  14. 14.
    Kwitt R, Meerwald P (2012) Salzburg texture image database, online;Accessed Sep 2012
  15. 15.
    Kwitt R, Uhl A (2010) Lightweight probabilistic texture retrieval 19(1):241–253Google Scholar
  16. 16.
    Kwitt R, Meerwald P, Uhl A (2011) Efficient texture image retrieval using copulas in a bayesian framework. IEEE Trans Image Process 20(7):2063 –2077CrossRefMathSciNetGoogle Scholar
  17. 17.
    Lee Y B, Park U, Jain A K, Lee S W (2012) Pill-ID: Matching and retrieval of drug pill images. Pattern Recog Lett 33(7):904–910CrossRefGoogle Scholar
  18. 18.
    Lew M, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: State of the art and challenges. ACM Trans Multimed Comput, Commun Appl 2(1):1–19CrossRefGoogle Scholar
  19. 19.
    Liu G H, Zhang L, Hou Y K, Li Z Y, Yang J Y (2010) Image retrieval based on multi-texton histogram. Pattern Recog 43(7):2380–2389CrossRefzbMATHGoogle Scholar
  20. 20.
    Liu Y, Zhang D, Lu G, Ma W (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recog 40(1):262–282. CrossRefzbMATHGoogle Scholar
  21. 21.
    Lumini A, Maio D (2000) Haruspex: an image database system for query-by-examples. In: 15th International Conference on Pattern Recognition, vol 4Google Scholar
  22. 22.
    Mäenpää T, Pietikäinen M (2004) Classification with color and texture: jointly or separatelyPattern Recog 37(8):1629–1640CrossRefGoogle Scholar
  23. 23.
    MIT (2010) Vistex database of textures,, online; Accessed Dec 2010
  24. 24.
    Mülller H, Michoux N, Bandon D, Geissbuhler A (2004) A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. Int J Med Inform 73(1):1–23Google Scholar
  25. 25.
    Ngo C W, Pong T C, Chin R T (2001) Exploiting image indexing techniques in DCT domain. Pattern Recog 34(9):1841–1851CrossRefzbMATHGoogle Scholar
  26. 26.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recog 29(1):51–59CrossRefGoogle Scholar
  27. 27.
    Permuter H, Francos J, Jermyn I H (2003) Gaussian mixture models of texture and colour for image database retrieval Proc. IEEE International conference on acoustics, speech, signla processing, vol 3, pp 569–572Google Scholar
  28. 28.
    Picard R, Kabir T, Liu F (1993) Real-time recognition with the entire brodatz texture database. In: IEEE international conference on computet vision pattern recognition (CVPR)Google Scholar
  29. 29.
    Poursistani P, Nezamabadi-pour H, Moghadam RA, Saeed M (2011). Image indexing and retrieval in JPEG compressed domain based on vector quantization. Math Comp ModelGoogle Scholar
  30. 30.
    Qi H, Li K, Shen Y, Qu W (2010) An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recog 43(6):2017–2027CrossRefzbMATHGoogle Scholar
  31. 31.
    Rafiee G, Dlay S, Woo W (2010) A review of content-based image retrieval. In: 7th International symposium on communication systems networks and digital signal processing (CSNDSP)Google Scholar
  32. 32.
    Rui Y, Huang T S, Chang S F (1999) Image retrieval: Current techniques, promising directions, and open issues. J Vis Commun Image Represent 10(1):39–62CrossRefGoogle Scholar
  33. 33.
    Smeulders A, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349 –1380CrossRefGoogle Scholar
  34. 34.
    Theoharatos C, Pothos V, Laskaris N, Economou G, Fotopoulos S (2006) Multivariate image similarity in the compressed domain using statistical graph matching. Pattern Recog 39(10):1892–1904CrossRefzbMATHGoogle Scholar
  35. 35.
    Tian Y, Mei L (2003) Image retrieval based on multiple features using wavelet. In: Fifth international conference on computational intelligence and multimedia applicationsGoogle Scholar
  36. 36.
    Vellaikal A, Kuo CC (1996) Joint spatial-spectral indexing for image retrieval. In: Proceedings of International Conference on Image Processing, vol 3,Google Scholar
  37. 37.
    Verdoolaege G, De Backer S, Scheunders P (2008) Multiscale colour texture retrieval using the geodesic distance between multivariate generalized gaussian models. In: 15th IEEE international conference on image processingGoogle Scholar
  38. 38.
    Wang X Y, Zhang B B, Yang H Y (2012) Content-based image retrieval by integrating color and texture features. Multimed Tools Appl:1–25Google Scholar
  39. 39.
    Wang X Y, Yang H Y, Li D M (2013) A new content-based image retrieval technique using color and texture information. Comput Electr Eng 39(3):746–761CrossRefMathSciNetGoogle Scholar
  40. 40.
    Yu H, Li M, Zhang HJ, Feng J (2002) Color texture moments for content-based image retrieval. In: Proceeding IEEE international conference on image processing (ICIP),Google Scholar
  41. 41.
    Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using color and texture fused features. Math Comput Model 54(3):1121–1127CrossRefGoogle Scholar
  42. 42.
    Zhong D, Defée I (2005) DCT histogram optimization for image database retrieval. Pattern Recogn Lett 26(14):2272–2281CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Cong Bai
    • 1
    Email author
  • Jinglin Zhang
    • 2
  • Zhi Liu
    • 3
    • 4
  • Wan-Lei Zhao
    • 5
  1. 1.College of Computer ScienceZhejiang University of TechnologyHangzhouChina
  2. 2.IETR UMR CNRS 6164INSA de Rennes, Université Européenne de BretagneRennesFrance
  3. 3.School of Communication and Information EngineeringShanghai UniversityShanghaiChina
  4. 4.IRISA/INRIA-RennesRennesFrance
  5. 5.INRIA-RennesRennesFrance

Personalised recommendations