Multimedia Tools and Applications

, Volume 76, Issue 6, pp 8031–8066 | Cite as

Noise robust and rotation invariant entropy features for texture classification

  • Mohammad Hossein ShakoorEmail author
  • Farshad Tajeripour


In this paper, a new formula is proposed that uses local entropy for texture feature extraction. This new method is similar to entropy; however, it calculates the local entropy of each local patch of textures. Entropy (ENT) is an attribute that measures the randomness of gray-level distribution of image. Entropy extracts dissimilarity of each local patch. In this paper, local entropy is compared to Local Binary Pattern (LBP) and local variance (VAR). All of these descriptors are rotation invariant and are used for extracting the features from each local neighborhood of textures. In spite of low accuracy of VAR and LBP the performance of ENT does not decrease significantly for noisy textures. In other words, ENT is more robust to noise than VAR and LBP. Implementations on Outex, UIUC, CUReT and MeasTex datasets show that entropy is more accurate than variance and LBP. Similar to VAR and LBP, ENT can be combined with other descriptors to improve the performance of classification. For almost all datasets that are used in implementation part, LBP/ENT is more accurate than LBP/VAR for normal and noisy textures. Also the ENT accuracy outperforms the accuracy of VAR and LBP and most of the advanced noise robust LBP versions for low Signal to Noise Ratio (SNR) values (SNR < 10). ENT feature is a continuous value so it is necessary to quantize to discrete value for histogram. The quantization and train step of ENT is the same as VAR.


Local entropy Variance Local binary pattern Texture classification Noise resistance 


  1. 1.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face recognition with local binary patterns: application to face recognition. IEEE Trans PatternAnal Mach Intell 28(12):2037–2041CrossRefzbMATHGoogle Scholar
  2. 2.
    Ahonen T, Pietikainen M (2007) In Proceedings of the Finnish Signal Processing Symposium, FINSIG 2007. Soft histograms for local binary patterns (Oulu, Finland, 2007). 1:1–4Google Scholar
  3. 3.
    Alahi A, Ortiz R, Vandergheynst P (2012) FREAK: Fast Retina Keypoint. IEEE Conf Comput Vis Pattern RecognGoogle Scholar
  4. 4.
    Anys H, He DC (1995) Evaluation of textural and multi polarization radar features for crop classification. IEEE Trans Geosci Remote Sens 33(5):1170–1181CrossRefGoogle Scholar
  5. 5.
    Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. Comput Vis ECCV 404–417Google Scholar
  6. 6.
    Chen JL, Kundu A (1994) Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden Markov model. IEEE Trans Pattern Anal Mach Intell 16(2):208–214CrossRefGoogle Scholar
  7. 7.
    Cohen FS, Fan Z, Attali S (1991) Automated inspection of textile fabrics using Textural models. IEEE Trans Pattern Anal Mach Intell 13(8):803–808CrossRefGoogle Scholar
  8. 8.
    Dana KJ, van Ginneken B, Nayar SK, Koenderink JJ (1999) Reflectance and texture of real world surfaces. ACM Trans Graph 18(1):1–34CrossRefGoogle Scholar
  9. 9.
    Deng H, Clausi DA (2004) Gaussian VZ-MRF rotation-invariant features for image classification. IEEE Trans Pattern Anal Mach Intell 26(7):951–955CrossRefGoogle Scholar
  10. 10.
    Désira C, Petitjeana C, Heuttea L, Thibervillea L, Salaüna M (2012) An SVM-based distal lung image classification using texture descriptors. Comput Med Imaging Graph 36:264–270CrossRefGoogle Scholar
  11. 11.
    Fathi A, Naghsh-Nilchi A (2012) Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recogn LettGoogle Scholar
  12. 12.
    Galloway M (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4(2):172–199CrossRefGoogle Scholar
  13. 13.
    Garding J, Lindeberg T (1996) Direct computation of shape cues using scale-adapted spatial derivative operators. IJCV 17(2):163–191CrossRefGoogle Scholar
  14. 14.
    Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 9(16):1657–1663MathSciNetGoogle Scholar
  15. 15.
    Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP Variance (LBPV) with global matching. Pattern Recogn J 43:706–719CrossRefzbMATHGoogle Scholar
  16. 16.
    Haralick RM, Shanmugam K, Its’HakDinstein (1979) Textural features for image classification. IEEE Trans Syst Man CybernGoogle Scholar
  17. 17.
    Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recogn 42(3):425–436CrossRefzbMATHGoogle Scholar
  18. 18.
    Huang X, Li SZ, Wang Y (2004) Shape localization based on statistical method using extended local binary patterns. Proc Int Conf Image Graph 184–187Google Scholar
  19. 19.
    Huang Y, Wang Y, Tan T (2006) Combining statistics of geometrical and correlative features for 3d face recognition. In Proceedings of the British Machine Vision Conference. BMVA Press, pp 90.1–90.10Google Scholar
  20. 20.
    Huang D, Wang Y, Wang Y (2007) A robust method for near infrared face recognition based on extended local binary pattern. Proc Int Symp Vis Comput 437–446Google Scholar
  21. 21.
    Ji Q, Engel J, Craine E (2000) Texture analysis for classification of cervix lesions. IEEE Trans Med Imaging 19(11):1144–1149CrossRefGoogle Scholar
  22. 22.
    Kashyap RL, Khotanzed A (1986) A model-based method for rotation invariant texture classification. IEEE Trans Pattern Anal Mach Intell 8(4):472–481CrossRefGoogle Scholar
  23. 23.
    Kylberg, Sintorn (2013) Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J Image Video Proc 17Google Scholar
  24. 24.
    Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278CrossRefGoogle Scholar
  25. 25.
    Leutenegger, Chli, Siegwart (2011) BRISK: Binary Robust Invariant Scalable Keypoints. ICCVGoogle Scholar
  26. 26.
    Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118MathSciNetCrossRefGoogle Scholar
  27. 27.
    Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  28. 28.
    Mikolajczyk K, Schmid C (2002) Affiane invariant interest point detector. Proc ECCV 1:128–142zbMATHGoogle Scholar
  29. 29.
    Mir AH, Hanmandlu M, Tandon SN (1995) Texture analysis of CT images. IEEE Eng Med Biol Mag 14Google Scholar
  30. 30.
    Murala S, Maheshwari RP, Bala subramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886MathSciNetCrossRefGoogle Scholar
  31. 31.
    Ojala T (1997) Nonparametric texture analysis using simple spatial operators, with applications in visual inspection. Acta Univ Ouluensis C 105Google Scholar
  32. 32.
    Ojala T, Mäenpää T, Pietikäinen M, Viertola J, Kyllönen J, Huovinen S (2002) Outex – new framework for empirical evaluation of textureanalysis algorithm. Proc Int Conf Pattern Recogn 701–706Google Scholar
  33. 33.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29(1):51–59CrossRefGoogle Scholar
  34. 34.
    Ojala T, Pietikainen M, Maenpaa TT (2002) Multi resolution gray-scale and rotation Invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefzbMATHGoogle Scholar
  35. 35.
    Pietikäinen M, Ojala T, Xu Z (2000) Rotation-invariant texture classification using feature distributions. Pattern Recogn 33(1):43–52CrossRefGoogle Scholar
  36. 36.
    Ren J, Jiang X, Yuan J (2013) Noise resistant local binary pattern with an embedded error correction mechanism. IEEE Trans Image Process 22(10):4049–4060MathSciNetCrossRefGoogle Scholar
  37. 37.
    Smith G (1998) MeasTex image texture database and test suite centre for sensor signal and information processing. Univ QldGoogle Scholar
  38. 38.
    Tajeripour F, Kabir E, Sheikhi A (2008) Fabric defect detection using modified local binary patterns. EURASIP J Adv Signal Process 8:1–12zbMATHGoogle Scholar
  39. 39.
    Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. Proc Int Work Anal Model Faces Gestures 168–182Google Scholar
  40. 40.
    Varma M, Garg R (2007) Locally invariant fractal features for statistical texture classification. Proc Int Conf Comput Vis 1–8Google Scholar
  41. 41.
    Varma M, Zisserman A (2003) Texture classification: are filter banks necessary? Proc Int Conf Comput Vis Pattern Recogn 691–698Google Scholar
  42. 42.
    Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Int J Comput Vis 62(1–2):61–81CrossRefGoogle Scholar
  43. 43.
    Varma M, Zisserrman A (2009) A statistical approach to material classification using image patch examplars. IEEE Trans Pattern Anal Mach Intell 31(11):2032–2047CrossRefGoogle Scholar
  44. 44.
    Xu Y, Ji H, Fermuller C (2005) A projective invariant for texture. Proc Int Conf Comput Vis Pattern Recogn 1932–1939Google Scholar
  45. 45.
    Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative patterns versus local binary patterns: face recognition with high-order local patterns descriptor. IEEE Trans Image Process 19(2):533–544MathSciNetCrossRefGoogle Scholar
  46. 46.
    Zhang J, Marszalek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213–238CrossRefGoogle Scholar
  47. 47.
    Zhao Y, Jia W, Hu RX, Min H (2013) Completed robust local binary pattern for texture classification. Neurocomputing 106:6876Google Scholar
  48. 48.
    Zhao G, Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 27(6):915–928CrossRefGoogle Scholar
  49. 49.
    Zhun C, Bichot C, Chen L (2013) Image region description using orthogonal combination of local binary patterns enhanced with color information. Pattern Recogn 46:1949–1963CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringShiraz UniversityShirazIran

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