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A novel pixel range calculation technique for texture classification

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Abstract

In this article, a fractal base method has been implemented as a texture descriptor. As, fractal deals with self-similarity, the proposed method is able to find the similarity of local patterns of various images of similar group and discriminate the images of dissimilar patterns. Images are converted to binary images by using the concept of local binary pattern. In this paper we have proposed a new approach for texture classification called Pixel Range Calculation (PRC) method, which has been implemented by converting the color images to gray images. Four benchmarking datasets, such as, ALOT (Amsterdam Library Of Textures), UMD (University of MarylanD), KTH-TIPS2b (Kungliga Tekniska Hogskolan-Textures under varying Illumination, Pose and Scale) and UIUC (University of Illinois at Urbana-Champaign) has been used for our experimental purpose. The proposed method along with two state of art method namely, Gliding Box Method (GBM) and Multi Fractal Spectrum (MFS) has been implemented and tested on the above-mentioned datasets. The experimental results based on the classification accuracy show the PRC technique of fractal dimension estimation method out performs the GBM and MFS. It has been observed that the computational complexity of PRC method is much less than other two state of art methods.

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References

  1. Allain C, Cloitre M (1991) Characterizing the lacunarity of random and deterministic fractal sets. Phys Rev A 44:3552–3558. https://doi.org/10.1103/PhysRevA.44.3552

    Article  MathSciNet  Google Scholar 

  2. ALOT texture dataset (n.d.): https://aloi.science.uva.nl/public_alot/

  3. Anantrasirichai N, Achim A, Morgan JE, Erchova I, Nicholson L (2013) SVM-based texture classification in Optical Coherence Tomography. In: 2013 IEEE 10th International Symposium on Biomedical Imaging. IEEE. pp. 1332–1335

  4. Brodatz texture dataset (n.d.): SIPI Image Database - Textures (usc.edu). https://sipi.usc.edu/database/database.php?volume=textures

  5. Bu X, Wu Y, Gao Z, Jia Y (2019) Deep convolutional network with locality and sparsity constraints for texture classification. Pattern Recogn 91:34–46. https://doi.org/10.1016/j.patcog.2019.02.003

    Article  Google Scholar 

  6. Dash S, Senapati MR, Jena UR (2018) K-NN based automated reasoning using bilateral filter based texture descriptor for computing texture classification. Egypt Informatics J 19:133–144. https://doi.org/10.1016/j.eij.2018.01.003

    Article  Google Scholar 

  7. Eichmann G, Kasparis T (1988) Topologically invariant texture descriptors. Comput Vision Graph Image Process 41:267–281. https://doi.org/10.1016/0734-189X(88)90102-8

    Article  Google Scholar 

  8. Gagnepain JJ, Roques-Carmes C (1986) Fractal approach to two-dimensional and three-dimensional surface roughness. Wear 109:119–126. https://doi.org/10.1016/0043-1648(86)90257-7

    Article  Google Scholar 

  9. Gupta Y, Lama RK, Lee S-W, Kwon G-R (2020) An MRI brain disease classification system using PDFB-CT and GLCM with kernel-SVM for medical decision support. Multimed Tools Appl 79:32195–32224. https://doi.org/10.1007/s11042-020-09676-x

    Article  Google Scholar 

  10. Hall-Beyer M (2017) Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. Int J Remote Sens 38:1312–1338. https://doi.org/10.1080/01431161.2016.1278314

    Article  Google Scholar 

  11. In Kim K, Jung K, Kim JH (2003) Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Trans Pattern Anal Mach Intell 25:1631–1639. https://doi.org/10.1109/TPAMI.2003.1251157

    Article  Google Scholar 

  12. Ji H, Yang X, Ling H, Xu Y, Ji H, Yang X, Ling H, Xu Y (2013) Wavelet Domain Multifractal Analysis for Static and Dynamic Texture Classification. IEEE Trans Image Process 22:286–299. https://doi.org/10.1109/TIP.2012.2214040

    Article  MathSciNet  MATH  Google Scholar 

  13. Kang X, Xiang X, Li S, Benediktsson JA (2017) PCA-based edge-preserving features for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55:7140–7151. https://doi.org/10.1109/TGRS.2017.2743102

    Article  Google Scholar 

  14. Keller JM, Chen S, Crownover RM (1989) Texture description and segmentation through fractal geometry. Comput Vision Graph Image Process 45:150–166. https://doi.org/10.1016/0734-189X(89)90130-8

    Article  Google Scholar 

  15. Khmag A, Ramli AR, Al-haddad SAR, Kamarudin N (2018) Natural image noise level estimation based on local statistics for blind noise reduction. Vis Comput 34:575–587. https://doi.org/10.1007/s00371-017-1362-0

    Article  Google Scholar 

  16. Kim SC, Kang TJ (2007) Texture classification and segmentation using wavelet packet frame and Gaussian mixture model. Pattern Recogn 40:1207–1221. https://doi.org/10.1016/j.patcog.2006.09.012

    Article  MATH  Google Scholar 

  17. Kirkby MJ (1983) The fractal geometry of nature. Benoit B. Mandelbrot. W. H. Freeman and co., San Francisco, 1982. No. of pages: 460. Price: £22.75 (hardback). Earth Surf Process Landf 8:406–406. https://doi.org/10.1002/esp.3290080415

    Article  Google Scholar 

  18. KTH-TIPS texture dataset (n.d.). https://www.csc.kth.se/cvap/databases/kth-tips/download.html

  19. Laine A, Fan J (1993) Texture classification by wavelet packet signatures. IEEE Trans Pattern Anal Mach Intell 15:1186–1191. https://doi.org/10.1109/34.244679

    Article  Google Scholar 

  20. Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18:1107–1118. https://doi.org/10.1109/TIP.2009.2015682

    Article  MathSciNet  MATH  Google Scholar 

  21. Liu X, Wang DL (2003) Texture classification using spectral histograms. IEEE Trans Image Process 12:661–670. https://doi.org/10.1109/TIP.2003.812327

    Article  Google Scholar 

  22. Liu L, Fieguth P, Guo Y, Wang X, Pietikäinen M (2017) Local binary features for texture classification: taxonomy and experimental study. Pattern Recogn 62:135–160. https://doi.org/10.1016/j.patcog.2016.08.032

    Article  Google Scholar 

  23. Liu C, Shao H, Wu M, Zhou Y, Shao Y, Wang X (2017) Multi-scale inherent variation features-based texture filtering. Vis Comput 33:769–778. https://doi.org/10.1007/s00371-017-1380-y

    Article  Google Scholar 

  24. Liu L, Chen J, Fieguth P, Zhao G, Chellappa R, Pietikäinen M (2019) From BoW to CNN: two decades of texture representation for texture classification. Int J Comput Vis 127:74–109. https://doi.org/10.1007/s11263-018-1125-z

    Article  Google Scholar 

  25. Liu L, Chen J, Zhao G, Fieguth P, Chen X, Pietikainen M (2019) Texture classification in extreme scale variations using GANet. IEEE Trans Image Process 28:3910–3922. https://doi.org/10.1109/TIP.2019.2903300

    Article  MathSciNet  MATH  Google Scholar 

  26. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29:51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  27. Ojala T, Pietikäinen M, Mäenpää T (2000) Gray scale and rotation invariant texture classification with local binary patterns. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. pp. 404–420

  28. 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 24:971–987. https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  29. Qian X, Hua X-S, Chen P, Ke L (2011) PLBP: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recogn 44:2502–2515. https://doi.org/10.1016/j.patcog.2011.03.029

    Article  Google Scholar 

  30. Quan Y, Xu Y, Sun Y (2014) A distinct and compact texture descriptor. Image Vis Comput 32:250–259. https://doi.org/10.1016/j.imavis.2014.02.004

    Article  Google Scholar 

  31. Quan Y, Xu Y, Sun Y, Luo Y (2014) Lacunarity analysis on image patterns for texture classification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society. pp. 160–167

  32. Quan Y, Sun Y, Xu Y (2017) Spatiotemporal lacunarity spectrum for dynamic texture classification. Comput Vis Image Underst 165:85–96. https://doi.org/10.1016/j.cviu.2017.10.008

    Article  Google Scholar 

  33. Raju P, Rao VM, Rao BP (2019) Optimal GLCM combined FCM segmentation algorithm for detection of kidney cysts and tumor. Multimed Tools Appl 78:18419–18441. https://doi.org/10.1007/s11042-018-7145-4

    Article  Google Scholar 

  34. Ranganath A, Mishra J (2017) New Approach for Estimating Fractal Dimension of Both Gary and Color Images. In: 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE. pp. 678–683

  35. Ranganath A, Senapati MR, Sahu PK (2020) Estimating the fractal dimension of images using pixel range calculation technique. Vis Comput 37:635–650. https://doi.org/10.1007/s00371-020-01829-1

    Article  Google Scholar 

  36. Sarkar N, Chaudhuri BB (1994) An efficient differential box-counting approach to compute fractal dimension of image. IEEE Trans Syst Man Cybern 24:115–120. https://doi.org/10.1109/21.259692

    Article  Google Scholar 

  37. UIUC texture dataset (n.d.): http://www-cvr.ai.uiuc.edu/ponce_grp/data/index.htm

  38. UMD texture dataset (n.d.): http://users.umiacs.umd.edu/~fer/website-texture/texture.htm

  39. Varma M, Zisserman A (2002) Classifying images of materials: achieving viewpoint and illumination Independence. In: Proceedings of the 7th European Conference on Computer Vision, May 2002, Copenhagen, Denmark

  40. Wang J, Fan Y, Li Z, Lei T (2019) Texture classification using multi-resolution global and local Gabor features in pyramid space. Signal Image Video Process 13:163–170. https://doi.org/10.1007/s11760-018-1341-6

    Article  Google Scholar 

  41. Xing Z, Jia H (2020) An improved thermal exchange optimization based GLCM for multi-level image segmentation. Multimed Tools Appl 79:12007–12040. https://doi.org/10.1007/s11042-019-08566-1

    Article  Google Scholar 

  42. Xu Y, Yang X, Ling H, Ji H (2010) A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE pp. 161–168

  43. Xu Y, Quan Y, Ling H, Ji H (2011) Dynamic texture classification using dynamic fractal analysis. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1219–1226

  44. Yong Xu, Hui Ji, Fermuller C (2006) A projective invariant for textures. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), pp 1932–1939. https://doi.org/10.1109/CVPR.2006.38

  45. Zhao X, Lin Y, Liu L, Heikkila J, Zheng W (2019) Dynamic texture classification using unsupervised 3D filter learning and local binary encoding. IEEE Trans Multimed 21:1694–1708. https://doi.org/10.1109/TMM.2018.2890362

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the support given by Veer Surendra Sai University of Technology, India under TEQIP-III.

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  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, India

    Abadhan Ranganath, Manas Ranjan Senapati & Pradip Kumar Sahu

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  1. Abadhan Ranganath
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Correspondence to Pradip Kumar Sahu.

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Ranganath, A., Senapati, M.R. & Sahu, P.K. A novel pixel range calculation technique for texture classification. Multimed Tools Appl 81, 17639–17667 (2022). https://doi.org/10.1007/s11042-022-12186-7

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