Multimedia Tools and Applications

, Volume 78, Issue 14, pp 18995–19018 | Cite as

Texture image Classification based on improved local Quinary patterns

  • Laleh Armi
  • Shervan Fekri-ErshadEmail author


Texture image classification is an active research topic in computer vision that play an important role in many applications such as visual inspection systems, object tracking, medical image analysis, image segmentation, etc. So far, there are many descriptors for texture image analysis such as local binary patterns (LBP). LBP is a nonparametric operator, which describes the local spatial structure and the local contrast of an image. Local quinary patterns (LQP) is one of the improved versions of LBP in terms of classification accuracy. Statistic input parameters and don’t providing significant binary patterns are some disadvantages of LQP. In this paper a new version of LBP is proposed, which is known as improved local quinary patterns (ILQP). In this paper, a new definition is proposed to divide local quinary codes to four binary patterns. Each extracted binary patterns represent a subset of local features. Also, a new algorithm is proposed here to provide dynamic thresholds in dividing process of LQP. The proposed approach is evaluated using Outex, and Brodatz data sets. Our approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy in comparison with most of the state-of-the-art texture classification approaches. Low computational complexity, rotation invariant, low impulse-noise sensitivity and high usability are advantages of the proposed texture analysis descriptor.


Texture image classification Local Quinary patterns Local binary patterns Feature extraction 



  1. 1.
    Ahonen T, Pietikäinen M (2007) Soft histograms for local binary patterns. In Proc. of Finnish Signal Processing SymposiumGoogle Scholar
  2. 2.
    Al-Sumaidaee S, Abdullah M, Al-Nima R, Dlay S, Chambers J (2017) Multi-gradient features and elongated quinary pattern encoding for image-based facial expression recognition. Pattern Recogn 71:249–263Google Scholar
  3. 3.
    Arivazhagan S, Ganesan L, Kumar TS (2006) Texture classification using ridgelet transform. Pattern Recogn Lett 27(16):1875–1883Google Scholar
  4. 4.
    Brodatz P (1996) Textures: A Photographic Album for Atists and Designers. Dover Publications, NewyorkGoogle Scholar
  5. 5.
    Bu H, Wang J, Huang XB (2009) Fabric defect detection based on multiple fractal features and support vector data description. Eng Appl Artif Intell 22(2):224–235Google Scholar
  6. 6.
    Chen J, Jain AK (1988) A structural approach to identify defects in textured images. In Proc. of IEEE International Conference on systems, manufacturing and cybernetics 29–32Google Scholar
  7. 7.
    Deep G, Kaur L, Gupta S (2018) Local quantized Extrema quinary pattern: a new descriptor for biomedical image indexing and retrieval. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(6):687–703Google Scholar
  8. 8.
    Eichkitz CG, Davies J, Amtmann J, Schreilechner MG, DeGroot P (2015) Grey level co-occurrence matrix and its application to seismic data. First Break 33(3):71–77Google Scholar
  9. 9.
    Fekri-Ershad S (2011) Color texture classification approach based on combination of primitive pattern units and statistical features. International Journal of Multimedia and its applications 3(3):1–13Google Scholar
  10. 10.
    Fekri-Ershad S (2012) Texture classification approach based on energy variation. International Journal of Multimedia Technology 2(2):52–55Google Scholar
  11. 11.
    Fekri-Ershad Sh (2012) Texture classification approach based on combination of edge & co-occurrence and local binary pattern. In proc. of Int'l Conference on Image Processing, Computer Vision and Pattern Recognition, 626–629Google Scholar
  12. 12.
    Fekri-Ershad S, Tajeripour F (2017) Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm. Sens Rev 37(1):33–42Google Scholar
  13. 13.
    Fekri-Ershad S, Tajeripour F (2017) Impulse-noise resistant color-texture classification approach using hybrid color local binary patterns and kullback-leibler divergence. Comput J 60(11):1633–1648Google Scholar
  14. 14.
    Fogel I, Sagi D (1989) Gabor filters as texture discriminator. Biol Cybern 61(2):103–113Google Scholar
  15. 15.
    Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663MathSciNetzbMATHGoogle Scholar
  16. 16.
    Hafiane A, Seetharaman G, Zavidovique B (2007) Median binary pattern for textures classification. In proc. of international conference on image analysis and recognition. Lect Notes Comput Sci 4633:387–398Google Scholar
  17. 17.
    Haralick RM, Shanmugam K (1973) Textural features for image classification. IEEE Transactions on systems, man, and cybernetics 6:610–621Google Scholar
  18. 18.
    Iakovidis DK, Keramidas EG, Maroulis D (2008) Fuzzy local binary patterns for ultrasound texture characterization. In proc. of international conference on image Analsysi and recognition. Lect Notes Comput Sci 5112:750–759Google Scholar
  19. 19.
    Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved LBP under bayesian framework. In Proc. of Third International conference on Image and Graphics 306–309Google Scholar
  20. 20.
    Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842Google Scholar
  21. 21.
    Mehta R, Egiazarian K (2016) Texture classification using dense micro-block difference. IEEE Trans Image Process 25(40):1604–1616MathSciNetzbMATHGoogle Scholar
  22. 22.
    Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 49(2):117–125Google Scholar
  23. 23.
    Nanni L, Brahnam S, Lumini A (2010) A local approach based on a local binary patterns variant texture descriptor for classifying pain states. Expert Syst Appl 37(12):7888–7894Google Scholar
  24. 24.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59Google Scholar
  25. 25.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987zbMATHGoogle Scholar
  26. 26.
    Ojala T, Maenppa T, Pietikainen M, Viertola J, Kyllonen J, Huovinen S (2002) Outex-new framework for empirical evaluation of texture analysis algorithms. In Proc. of 16th International Conference on Pattern Recognition 701–706 Downloadable:
  27. 27.
    Pietikäinen M, Ojala T, Xu Z (2000) Rotation-invariant texture classification using feature distributions. Pattern Recogn 33(1):43–52Google Scholar
  28. 28.
    Rajesh R, VeerappanJ, Sujitha S, Kumar EA (2012) Classification and retrieval of images using texture features. In Proc. of Third InternationalConference on Computing Communication and Networking Technologies 1–5Google Scholar
  29. 29.
    Rampun A, Morrow P, Scotney B, Winder J (2017) Breast density classification using multiresolution local quinary patterns in mammograms. In Proc. of Annual Conference on Medical Image Understanding and Analysis 365–376
  30. 30.
    Ren J, Jiang X, Yuan J (2013) Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans Image Process 22(10):4049–4060MathSciNetzbMATHGoogle Scholar
  31. 31.
    Shakoor MH, Tajeripour F (2017) Noise robust and rotation invariant entropy features for texture classification. Multimed Tools Appl 76(60):8031–8066Google Scholar
  32. 32.
    Shanmugavadivu P, Sivakumar V (2012) Fractal dimension based texture analysis of digital images. Procedia Engineering 38:2981–2986Google Scholar
  33. 33.
    Tajeripour F, Fekri-Ershad S (2014) Developing a novel approach for stone porosity computing using modified local binary patterns and single scale retinex. Arab J Sci Eng 39(2):875–889Google Scholar
  34. 34.
    Tajeripour F, Kabir E, Sheikhi A (2008) Fabric defect detection using modified local binary patterns. EURASIP Journal on Advances in Signal Processing 08(1):783–789zbMATHGoogle Scholar
  35. 35.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transaction on Image Processing 19(6):1635–1650MathSciNetzbMATHGoogle Scholar
  36. 36.
    The KTH-TIPS and KTH-TIPS2 image databases (2006) Downloadable:
  37. 37.
    Tuceryan M, Jain AK (1993) Texture analysis handbook of pattern recognition and computer vision, World Scientific Publishing Company(2nd Edition):207–248Google Scholar
  38. 38.
    Vipparthi SK, Nagar SK (2014) Color directional local quinary patterns for content based indexing and retrieval. Human-centric computing and. Inf Sci 4(1):1–13Google Scholar
  39. 39.
    Wang T, Dong Y, Yang C, Wang L, Liang L, Zheng L, Pu J (2018) Jumping and refined local pattern for texture classification. IEEE Access 6:4416–64426Google Scholar
  40. 40.
    Wen W, Xia A (1999) Verifying edges for visual inspection purposes. Pattern Recogn Lett 20(3):315–328Google Scholar
  41. 41.
    Yuan J, Zhu H, Gan Y, Shang L (2014) Enhanced local ternary pattern for texture classification. In proc. of international conference on intelligent computing theory. Lect Notes Comput Sci 8588:443–448Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Computer Engineering, Najafabad BranchIslamic Azad UniversityNajafabadIran
  2. 2.Big Data Research Center, Najafabad BranchIslamic Azad UniversityNajafabadIran

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