CFP last date
20 March 2024
Reseach Article

Segmentation of Multi-Textured Images using Optimized Local Ternary Patterns

by G. Madasamy Raja, V. Sadasivam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 16
Year of Publication: 2014
Authors: G. Madasamy Raja, V. Sadasivam
10.5120/16680-6789

G. Madasamy Raja, V. Sadasivam . Segmentation of Multi-Textured Images using Optimized Local Ternary Patterns. International Journal of Computer Applications. 95, 16 ( June 2014), 22-29. DOI=10.5120/16680-6789

@article{ 10.5120/16680-6789,
author = { G. Madasamy Raja, V. Sadasivam },
title = { Segmentation of Multi-Textured Images using Optimized Local Ternary Patterns },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 16 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 22-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number16/16680-6789/ },
doi = { 10.5120/16680-6789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:08.650833+05:30
%A G. Madasamy Raja
%A V. Sadasivam
%T Segmentation of Multi-Textured Images using Optimized Local Ternary Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 16
%P 22-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Texture segmentation is a process of segmenting an image into differently textured regions. This paper is aimed at the segmentation of multitextured images by using Optimized Local Ternary Patterns (OLTP), a new texture model which is recently proposed for texture analysis. This paper uses unsupervised texture segmentation method with the application of optimized local ternary patterns for finding the dissimilarity of adjacent image regions during the segmentation process. The performance of this recently proposed texture measure OLTP is evaluated, compared with other texture models Texture Spectrum (TS) and Local Binary Patterns (LBP) and found to be the best.

References
  1. S. Kothainachiar and R. S. D. Wahita Banu, "A novel image segmentation based on a combination of color and texture features," GVIP Journal, vol. 7, no. 2, Aug. 2007, pp. 45–51.
  2. Dong Chen He and Li Wang, "Texture classification using texture spectrum," Pattern Recognition, vol. 23, no. 8, 1990, pp. 905–910.
  3. G. N. Srinivasan and G. Shobha, "Statistical Texture Analysis," in Proc. World Academy Of Science, Engineering And Technology, 2008, vol. 36, pp. 1264-1269.
  4. S. Arivazhagan and L. Ganesan, "Texture segmentation using wavelet transform," Pattern Recognition Letters, vol. 24, no. 16, Dec. 2003, pp. 3197–3203.
  5. P. P. Raghu and B. Yegnanarayana, "Segmentation of Gabor filtered textures using deterministic relaxation," IEEE Transactions on Image Processing, vol. 5, no. 12, Dec. 1996, pp. 1625–1636.
  6. R. Jain, Kasturi and B. G. Schunch, Machine Vision. McGraw Hill, 1995, pp. 234–240.
  7. M. Tuceryan and A. K. Jain, "Texture Analysis," in The Handbook of Pattern Recognition and Computer Vision, chapter 2. 1. C. H. Chen, L. F. Pau and P. S. P. Wang, Ed. Singapore: World Scientific Publishing Co. , 1993, pp. 235-276.
  8. P. C. Chen and T. Pavlidis, "Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm," Computer Graphics and Image Processing, vol. 10, no. 2, 1979, pp. 172–182.
  9. M. Spann and R. Wilson, "A quad-tree approach to image segmentation which combines statistical and spatial information," Pattern Recognition, vol. 18, no. 3/4, 1985, pp. 257–269.
  10. D. C. He and L. Wang, "Unsupervised textural classification of images using the Texture Spectrum," Pattern Recognition, vol. 25, no. 3, Mar. 1992, pp. 247–255.
  11. T. Ojala and M. Pietikainen, "Unsupervised texture segmentation using feature distributions," Pattern recognition, vol. 32, no. 3, Mar. 1999, pp. 477-486.
  12. D. M. Tsai, S. K. Wu and M. C. Chen, "Optimal Gabor filter design for texture segmentation using stochastic optimization," Image and Vision computing, vol. 19, no. 5, Apr. 2001, pp. 299-316.
  13. T. Randen and J. H. Husoy, "Texture segmentation using filters with optimized energy separation," IEEE transactions on Image Processing, vol. 8, no. 4, Apr. 1999, pp. 571-582.
  14. L. Ma, L. P. Lu and L. Zhu, "Unsupervised texture segmentation based on multi-scale Local Binary Patterns and FCMs clustering," in IET International Conference on Wireless, Mobile and Multimedia Networks Proceedings (ICWMMN 2006), Hangzhou, China, pp. 1-4.
  15. K. H. Kim, B. S. Sharif and E. G. Chester, "Unsupervised texture analysis using a robust stochastic image model," in Proc. 1997 6th International Conference on Image Processing and its Applications, Dublin, vol. 2, pp. 613 – 617.
  16. J. Mao and A. K. Jain, "Texture classification and segmentation using multiresolution simultaneous autoregressive models," Pattern Recognition, vol. 25, no. 2, Feb. 1992, pp. 173–188.
  17. G. Fan and X. Xia, "Wavelet-based texture analysis and synthesis using hidden Markov model," IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol. 50, no. 1, 2003, pp. 106-120.
  18. H. C. Chen, W. J. Chien and S. J. Wang, "Contrast-based color image segmentation," IEEE Signal Processing Letters, vol. 11, no. 7, July 2004, pp. 641-644.
  19. Y. Chang, D. Lee and Y. Wang, "Color-texture segmentation of medical images based on local contrast information," in Proc. IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, Honolulu, HI, 2007, pp. 488–493.
  20. L. Garcia Ugarriza, E. Saber, S. R. Vantaram, V. Amuso, M. Shaw and R. Bhaskar, "Automatic image segmentation by Dynamic Region Growth and Multiresolution Merging," IEEE Transactions on Image Processing, vol. 18, no. 10, Oct. 2009, pp. 2275–2288.
  21. W. H. Liao and T. J. Young, "Texture classification using Uniform Extended Local Ternary Patterns," in 12th IEEE International Symposium on Multimedia(ISM 2010), Taiwan, pp. 191–195.
  22. R. Suguna and P. Anandhakumar, "A rotation invariant pattern operator for texture characterization," International Journal of Computer Science and Network Security (IJCSNS), vol. 10, no. 4, Apr. 2010, pp. 120–129.
  23. F. Ahmed, E. Hossain, A. S. M. H. Bari and M. S. Hossen, "Compound Local Binary Pattern (CLBP) for rotation invariant texture classification," International Journal of Computer Applications (IJCA), vol. 33, no. 6, Nov. 2011, pp. 5-10.
  24. G. Madasamy Raja and V. Sadasivam, "Optimized Local Ternary Patterns: A New Texture Model with Set of Optimal Patterns for Texture Analysis," Journal of Computer Science, vol. 9, no. 1, Jan 2013, pp. 1–14.
  25. T. Ojala, M. Pietikainen and D. Harwood, "A comparative study of texture measures with classification based on feature distributions", Pattern Recognition, vol. 29, no. 1, Jan 1996, pp. 51-59.
  26. P. Brodatz, Textures, A Photographic Album for Artists and Designers, Dover Publications, New York, 1966.
Index Terms

Computer Science
Information Sciences

Keywords

Texture Spectrum Texture Segmentation Kullback-Leibler Distance Local Binary Patterns Optimized Local Ternary Patterns Brodatz Textures.