Effect of gray-level re-quantization on co-occurrence based texture analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Gray-level co-occurrence matrices (GLCM) are widely used for texture analysis. The number of gray-levels used while computing GLCM is an important parameter but is often ignored. It is believed that the higher the number of gray-levels used, the better is the performance of the GLCM-based features. Contrary to this belief, we observed that using more gray levels than the actual range of pixel values in the image can give erroneous results. In this paper, we show how this occurs and discuss a way around this problem.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages585-588
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
CountryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

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Keywords

  • Gray-level co-occurrence matrix
  • Histogram
  • Re-quantization
  • Texture analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Patel, M. B., Rodriguez, J. J., & Gmitro, A. F. (2008). Effect of gray-level re-quantization on co-occurrence based texture analysis. In 2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings (pp. 585-588). [4711822] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2008.4711822