Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Extraction of Texture Information from Fuzzy Run Length Matrix

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 55 - Number 1
Year of Publication: 2012
Y. Venkateswarlu
B. Sujatha
V. Vijaya Kumar

Y Venkateswarlu, B Sujatha and Vijaya V Kumar. Article: Extraction of Texture Information from Fuzzy Run Length Matrix. International Journal of Computer Applications 55(1):36-41, October 2012. Full text available. BibTeX

	author = {Y. Venkateswarlu and B. Sujatha and V. Vijaya Kumar},
	title = {Article: Extraction of Texture Information from Fuzzy Run Length Matrix},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {55},
	number = {1},
	pages = {36-41},
	month = {October},
	note = {Full text available}


For a precise texture classification and analysis, a run length matrix is constructed on the Local Binary pattern using fuzzy principles in the present paper. The proposed Run Length Matrix on Fuzzy LBP (RLM-FLBP) overcomes the disadvantages of the previous run length methods of texture classification that exist in the literature. LBP is a widely used tool for texture classification based on local features. The LBP does not provide greater amount of discriminate information of the local structure and it has a various other disadvantages. The main disadvantage of LBP is, that it compares the centre pixel value with its neighbors to derive the one of the three possible values {0, 1, 2}. The basic drawback of this comparison is that it is very sensitive to noise. And a major contrast between the central pixel and its surroundings are easily resulted by the slight fluctuations above or below the value of the Centre Pixel (CP) and its surroundings. To overcome this problem and to represent the missing local information effectively in the LBP, the present study introduced the concept of fuzzy logic on LBP. This overcomes the problem related to noise and contrast. The proposed method initially converts the 3×3 neighborhood in to fuzzy LBP. In the second stage the proposed method constructs the Run Length Matrix on Fuzzy LBP (RLM-FLBP). On these RLM-FLBP texture features are evaluated for a precise texture classification.


  • M. M. Galloway, "Texture analysis using gray level run lengths," Comput. Graphics Image Process. , vol. 4, pp. 172–179, June 1975.
  • Ahonen T. , Hadid A. and Pietikainen M. , "Face Recognition with Local Binary Patterns," Computer Vision, ECCV Proceedings, pp. 469-481, 2004.
  • Ahonen T. , Pietikainen M. , Hadid A. and Maenpaa T. , "Face Recognition Based on the Appearance of Local Regions," 17th International Conference on Pattern Recognition III: pp. 153-156, 2004.
  • Feng X. , Hadid A. and Pietikainen M. , "A Coarse-to-Fine Classification Scheme for Facial Expression Recognition," Image Analysis and Recognition, ICIAR 2004 Proceedings, Lecture Notes in Computer Science 3212 II: pp. 668-675, 2004.
  • Feng X. , Pietikainen M. and Hadid A. , "Facial Expression Recognition with Local Binary Patterns and Linear Programming," Pattern Recognition and Image Analysis 15 pp. 550-552, 2005.
  • Hadid A. , Pietikainen M. and Ahonen T. , "A Discriminative Feature Space for Detecting and Recognizing Faces," IEEE Conference on Computer Vision and Pattern Recognition II: pp. 797-804, 2004.
  • Heikkila M. , Pietikainen M. and Heikkila J. , "A Texture-Based Method for Detecting Moving Objects," The 15th British Machine Vision Conference I: pp. 187-196, 2004.
  • Takala V. , Ahonen T. and Pietikainen M. , "Block-Based Methods for Image Retrieval Using Local Binary Patterns," Image Analysis, SCIA 2005 Proceedings, Lecture Notes in Computer Science, 2005.
  • Turtinen M. and Pietikainen M. , "Visual Training and Classification of Textured Scene Images," 3rd International Workshop on Texture Analysis and Synthesis pp. 101-106, 2003.
  • Maenpaa T. and Pietikainen, M. , "Texture Analysis with Local Binary Patterns," Handbook of Pattern Recognition and Computer Vision, 3rd edn. World Scientific pp. 197-216, 2005.
  • Ojala T. , Pietikainen M. , Harwood D. , "A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition," pp. 51-59, 1996.
  • Ojala T. , Pietikainen M. , Maenpaa T. , "Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence 24 pp. 971-987, 2002.
  • J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. Syst. , Man, Cybern. , vol. SMC-6, pp. 269–285, 1976.
  • Ojala T. , Pietikäinen M. , Mäenpää T. , Viertola J. , Kyllönen J. , Huovinen S. , "Outex-new framework for empirical evaluation of texture analysis algorithms," In: Proc. 16th Int. Conf. on Pattern Recognition, vol. 1, pp:701–706, 2002(b).
  • Antonio Fern´andez, Ovidiu Ghita, Elena Gonz´alez, Francesco Bianconi, Paul F. Whelan, "Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification," Machine Vision and Applications, 2011.
  • Xiaoou Tang, "Texture Information in Run-Length Matrices," IEEE, pp: 1602-1609, 1998.
  • Ramana Reddy B. V. , Radhika Mani M. Sujatha B. , Vijaya Kumar V. , "Texture Classification Based on Random Threshold Vector Technique," International Journal of Multimedia and Ubiquitous Engineering Vol. 5, No. 1, January, 2010