Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 6, pp 6581–6605 | Cite as

Rotation invariant curvelet based image retrieval & classification via Gaussian mixture model and co-occurrence features

  • M. Alptekin Engin
  • Bulent CavusogluEmail author
Article
  • 102 Downloads

Abstract

Demand for better retrieval methods continue to outstrip the capabilities of available technologies despite the rapid growth of new feature extraction techniques. Extracting discriminatory features that contain texture specific information are of crucial importance in image indexing. This paper presents a novel rotation invariant texture representation model based on the multi-resolution curvelet transform via co-occurrence and Gaussian mixture features for image retrieval and classification. To extract these features, curvelet transform is applied and the coefficients are obtained at each scale and orientation. The Gaussian mixture model (GMM) features are computed from each of the sub bands and co-occurrence features are computed for only specific sub band. Rotation invariance is provided by applying cycle-shift around the GMM features. The proposed method is evaluated on well-known databases such as Brodatz, Outex_TC_00010, Outex_TC_00012, Outex_TC_00012horizon, Outex_TC_00012tl84, Vistex and KTH-TIPS. When the feature vector is analyzed in terms of its size, it is observed that its dimension is smaller than that of the existing rotation-invariant variants and it has a very good performance. Simulation results show a good performance achieved by combining different techniques with the curvelet transform. Proposed method results in high degree of success rate in classification and in precision-recall value for retrieval.

Keywords

Curvelet transform Image retrieval Image classification 

Notes

References

  1. 1.
    Arivazhagan S, Ganesan L, Kumar TGS (2006) Texture classification using Curvelet statistical and co-occurrence features. Pattern Recogn ICPR 2006. 18th Int Conf 2:938–941CrossRefGoogle Scholar
  2. 2.
    Bapat Malao S, Shahane NM (2013) Curvelet based image indexing and retrieval. Int J Emerg Trends Technol Comput Sci (IJETTCS) 2(2)Google Scholar
  3. 3.
    H. Bay, T. Tuytelaars, L. Van Gool, (2006) Surf: speeded up robust features. Proc Eur Conf Comput VisionGoogle Scholar
  4. 4.
    Brilakis IK, Soibelman L, Shinagawa Y (2006) Construction site image retrieval based on material cluster recognition. Adv Eng Inform 20(4):443–452CrossRefGoogle Scholar
  5. 5.
    Brodatz P (1966) Textures: a photographic album for artists and designers. Dover, NewYorkGoogle Scholar
  6. 6.
    Candès E (1998) Ridgelets: theory and applications, Ph.D. thesis, department of statistics, Stanford universityGoogle Scholar
  7. 7.
    Candès E (1999) Harmonic analysis of neural networks. Appl Comput Harmon Anal 6(2):197–218MathSciNetCrossRefGoogle Scholar
  8. 8.
    Candès E, Donoho D (1999) Ridgelets: a key to higher-dimensional intermittency? Philos Trans R Soc London A, math Phys Eng Sci 357(1760):2495–2509MathSciNetCrossRefGoogle Scholar
  9. 9.
    Candès E, Donoho D (2000) Curvelets-a surprisingly effective nonadaptive representation for objects with edges. In: Cohen A, Rabut C, Schumaker L (eds) Curves and surface fitting: Saint-Malo 1999. Vanderbilt Univ. Press, Nashville, pp 105–120Google Scholar
  10. 10.
    Candes EJ, Donoho DL (2002) New tight frames of Curvelets and optimal representations of objects with smooth singularities. Technical Report, Stanford UniversityGoogle Scholar
  11. 11.
    Candès E, Donoho D (2004) New tight frames of curvelets and optimal representations of objects with piecewise singularities. Commun Pure Appl Math 57(2):219–266MathSciNetCrossRefGoogle Scholar
  12. 12.
    Candès E, Donoho D (2005) Continuous curvelet transform. I. Resolution of the wavefront set. Appl Comput Harmon Anal 19(2):162–197MathSciNetCrossRefGoogle Scholar
  13. 13.
    Candès E, Donoho D (2005) Continuous curvelet transform. II discretization and frames. Appl Comput Harmon Anal 19(2):198–222MathSciNetCrossRefGoogle Scholar
  14. 14.
    Candès E, Demanet L, Donoho D, Ying L (2006) 2006, Fast discrete Curvelet tansforms. Multiscale Model Simul 5(3):861–899MathSciNetCrossRefGoogle Scholar
  15. 15.
    Cavusoglu B (2014) Multiscale texture retrieval based on low-dimensional and rotation-invariant features of curvelet transform. EURASIP. J Image Video Process 2014:22CrossRefGoogle Scholar
  16. 16.
    Chun JC, Kim WG (2013) Textile image retrieval using composite feature vectors of color and wavelet transformed textural property. Appl Mech Mater 333-335:822–827CrossRefGoogle Scholar
  17. 17.
    Do MN, Vetterli M (2002) Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans Image Process 11MathSciNetCrossRefGoogle Scholar
  18. 18.
    Gasteratos A, Zafeiridis P, Andreadis IT (2004) An intelligent system for aerial image retrieval and classification. In: Vouros G, Panayiotopoulos T (eds) SETN 2004. LNCS, vol 3025. Springer, Heidelberg, pp 63–71Google Scholar
  19. 19.
    Gomez F, Romero E (2011) Rotation invariant texture characterization using a curvelet based descriptor. Pattern Recogn Lett 32:2178–2186CrossRefGoogle Scholar
  20. 20.
    Grigorescu SE, Petkov N, Kruizinga P (2002) Comparison of texture features based on Gabor filters. IEEE Trans Image Process 11(10):1160–1167MathSciNetCrossRefGoogle Scholar
  21. 21.
    Guo ZH, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn 43:706–719CrossRefGoogle Scholar
  22. 22.
    Haley GM, Manjunath BS (1999) Rotation-invariant texture classification using acomplete space-frequency model. IEEE Trans Image Processing 8:255–269CrossRefGoogle Scholar
  23. 23.
    Han J, Ma K-K (2007) Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image Vis Comput 25(9):1474–1481CrossRefGoogle Scholar
  24. 24.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. Syst Man Cybernet, IEEE Trans SMC-3(6):610–621CrossRefGoogle Scholar
  25. 25.
    E Hayman, B Caputo, M Fritz, J-O Eklundh On the significance of real-world conditions for material classification, in Computer vision, ed. by T Pajdla, J Matas (Springer, Berlin Heidelberg, 2004), pp. 253–266Google Scholar
  26. 26.
    Iqbal K, Odetayo MO, James A (2012) Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics. J Comput Syst Sci 78(4):1258–1277MathSciNetCrossRefGoogle Scholar
  27. 27.
    Kaushik M, Sharma R, Vidhyarthi A (2012) Article: analysis of spatial features in CBIR system. Int J Comput Appl 54(17):11–15Google Scholar
  28. 28.
    Lazebnik S, Schmid C, Ponce J (2003) A sparse texture representation using affine-invariant regions. IEEE Comput Soc Conf Comput Vision Pattern Recogn 2:319–324Google Scholar
  29. 29.
    Li Y, Yang Q, Jiao R (2010) Image compression scheme based on curvelet transform and support vector machine. Expert Syst Appl 37(4):3063–3069CrossRefGoogle Scholar
  30. 30.
    Lowe, D.G., "Object recognition from local scale-invariant features," in Comput Vision 1999. Proc Seventh IEEE Int Conf, vol. 2, no., pp.1150–1157 vol.2, 1999Google Scholar
  31. 31.
    Machajdik J, Hanbury A (2010) Affective image classification using features inspired by psychology and art theory. In Proceedings of the international conference on multimedia (MM '10). ACM, New York, NY, USA, 83–92Google Scholar
  32. 32.
    Mikolajczyk K, Schmid C (2003) A performance evaluation of local descriptors. Proc Comput Vision Pattern RecognGoogle Scholar
  33. 33.
    Henning Müller, Nicolas Michoux, David Bandon, Antoine Geissbuhler Erratum to “A review of content-based image retrieval systems in medical applications—Clinical benefits and future directions” [Int J Med Inform 73 (1) (2004) 1–23] Int J Med Inform, Volume 78, Issue 9, September 2009, Page 638Google Scholar
  34. 34.
    Rajlaxmi N, Lokhande SS (2015) Content based image retrieval using spectral feature extraction methods. SSRG Int J Electron Commun Eng (SSRG-IJECE) 2(4)Google Scholar
  35. 35.
    Shen L, Yin Q (2009) Texture classification using Curvelet transform. Proc 2009 Int Sym Inform Process (ISIP’09) Huangshan, P R China 21-23:319–324Google Scholar
  36. 36.
    Sumana IJ, Islam MM, Zhang D, Guojun L (2008) Content based image retrieval using curvelet transform. Multimed Signal Process 2008 IEEE 10th Workshop: 11–16Google Scholar
  37. 37.
    Tan TN (1995) Geometric transform invariant texture analysis. Proc SPIE 2(488):475–485CrossRefGoogle Scholar
  38. 38.
    Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2018) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transport Syst 19(1):284–295CrossRefGoogle Scholar
  39. 39.
    Zhang DS, Islam MM, GJ L, Sumana IJ (2012) Rotation invariant curvelet features for region based image retrieval. Int J Comput Vis 98:187–201MathSciNetCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Electrical and Electronics Engineering, Engineering FacultyBayburt UniversityBayburtTurkey
  2. 2.Department of Electrical and Electronics Engineering, Engineering FacultyAtaturk UniversityErzurumTurkey

Personalised recommendations