The week's pick
Reseach Article
Study and Review of Various Image Texture Classification Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 16 |
| Year of Publication: 2013 |
| Authors: Sandip S. Patil, Harshal S. Patil |
10.5120/13197-0897
|
Sandip S. Patil, Harshal S. Patil . Study and Review of Various Image Texture Classification Methods. International Journal of Computer Applications. 75, 16 ( August 2013), 33-38. DOI=10.5120/13197-0897
Abstract
Pattern is an arrangement of features which are defined by various characteristics of image such as shape, color and texture. Texture is an important characteristic for image analysis. The major trend of the research today in terms of feature extraction for classification is accuracy oriented, however usually the newer algorithms that promises better accuracy is much more complicated in its calculations and often sacrifices the speed of the algorithm. This paper contains study and review of various techniques used for feature extraction and texture classification. The objective of study is to find technique or combination of techniques to reduce complexity, speed while increasing the accuracy at the same time. Here we are studying and reviewing the three feature extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter method. Also two classification methods KNN and SVM are used on the texture datasets Brodatz, CUReT, VisTex and OuTex for the experimental purpose.
References
- R. J. Bhiwani, S. M. Agrawal and M. A. Khan,2010. "Texture Based Pattern Classification", International Journal of Computer Applications, Vol. 1 – No. 1, pp. 60-62.
- Andy song,2003. "Texture Classification: A Genetic Programming Approch", April 9, .
- Prasetiyo, Marzuki Khalid, Rubiyah Yusofand Fabrice Meriaudeau,2010. "A Comparative Study of Feature Extraction Methods for Wood Texture Classification", Sixth International Conference on Signal-Image Technology and Internet Based Systems, vol. 1, pp. 23-29.
- Jing Yi Tou, Yong Haur Tay and Phooi Yee Lau,2009. "Recent Trends in Texture Classification: A Review" Symposium on Progress in Information & Communication Technology, pp. 63-68.
- J. Y. Tou, Y. H. Tay, and P. Y. Lau,2009. "Gabor Filters as Feature Images for Covariance Marix on Texture Classification Problem," ICONIP 2008, vol. 5507, pp. 745-751.
- C. Chen, C. Chen and C. Chen, 2006. "A Comparison of Texture Features Based on SVM and SOM," ICPR, vol. 2, pp. 630-633.
- P. Brodatz, 1996. Textures: A Photographic Album for Artists and Designers, Dover, New York.
- Scott Blunsden, 2004. "Texture Classification using Non-Parametric Markov Random Fields" MSUI University of Edinburgh.
- Information about Brodatz texture database: http://www. ux. uis. no/~tranden/brodatz. html
- Theory regarding Brodatz texture database: http://www. texturesynthesis. com/meastex/imgs/brodatz. html
- Information about CUReT texture dataset: http://www1. cs. columbia. edu/CAVE/software/curet
- The Vision Texture (VisTex) dataset is prepared by the Massachusetts Institute of Technology: http://vismod. media. mit. edu/vismod/imagery/VisionTexture/
- Information about outex texture dataset: http://www. outex. oulu. fi/temp/orig. html
- S. Liao, M. W. K. Law, A. C. S. Chung, 2009. "Dominant Local Binary Patterns for Texture Classification," IEEE TIP, vol. 18, no. 5, pp. 1107-1118.
- Zhao et al, 2011. "Texture Classification Based on Completed Modelling of Local Binary Pattern" IEEE International Conference on Computational and Information Science, vol. 2, pp. 268-271.
- J. Y. Tou, Y. H. Tay and P. Y. Lau, 2009. "A Comparative Study for Texture Classification Techniques on Wood Species Recognition Problem" IEEE Fifth International Conference on Natural Computation, pp. 8-12.
- O. Tuzel, F. Porikli and P. Meer, 2006. "Region Covariance: A Fast Desscriptor for Detection and Classification", European Conference on Computer Vision, vol. 1, pp. 697-704.
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