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     Research Journal of Applied Sciences, Engineering and Technology


Rotation Scale Invariant Texture Classification for a Computational Engine

C. Vivek and S. Audithan
PRIST University, Tanjore, Tamilnadu, India
Research Journal of Applied Sciences, Engineering and Technology  2014  13:1572-1577
http://dx.doi.org/10.19026/rjaset.8.1135  |  © The Author(s) 2014
Received: June ‎11, ‎2014  |  Accepted: July ‎09, ‎2014  |  Published: October 05, 2014

Abstract

Texture analysis is a highly significant area in the arena of computer vision and connected pitches. Not the least, classification is also equally important and laudable zone in the area of understanding the texture pattern and is gaining a lot of interest among the researchers in the field of computer vision. It finds a widespread application in area of pattern classification, robotic applications, textile industries etc. In this study, rotation invariant texture has been analyzed and a novel Rotation scale invariant texture classification algorithm has been proposed and tested which is found to be very efficacious and improved results are obtained with the same. The proposed algorithm has been made to undergo testing with standard data sets as UMD dataset, Vision Texture (VisTex), UIUC dataset. The results are discussed clearly with a line of justification being drawn.

Keywords:

Invariant texture classification , texture , texture classification, texture data sets,


References

  1. Campisi, P., A. Neri, C. Panci and G. Scarano, 2004. Robust rotation-invariant texture classification using a model based approach. IEEE T. Image Process., 13(6): 782-791.
    CrossRef    PMid:15648869    
  2. Carr, J.R. and F.P. De Miranda, 1998. The semivariogram in comparison to the co-occurrence matrix for classification of image texture. IEEE T. Geosci. Remote, 36: 1945-1952.
    CrossRef    
  3. Coggins, J.M. and A.K. Jain, 1985. A spatial filtering approach to texture analysis. Pattern Recogn. Lett., 3: 195-203.
    CrossRef    
  4. Deng, H. and D.A. Clausi, 2004. Gaussian MRF rotation-invariant features for image classification. IEEE T. Pattern Anal., 26(7): 951-955.
    CrossRef    PMid:18579954    
  5. Fang, Y.K., Y. Fu, C.J. Sun and J.L. Zhou, 2010. LP boost with strong classifiers. Int. J. Comput. Intell. Syst., 3: 88-100.
    CrossRef    
  6. Fang, Y., Y. Fu, C. Sun and J. Zhou, 2011. Improved boosting algorithm using combined weak classifiers. J. Comput. Inform. Syst., 7: 1455-1462.
  7. Haralick, R.M., K. Shanmugam and I. Dinstein, 1973. Textural features for image classification. IEEE T. Syst. Man Cyb., SMC-3: 610-621.
    CrossRef    
  8. He, D.C. and L. Wang, 1992. Unsupervised textural classification of images using the texture spectrum. Pattern Recogn., 25(3): 247-255.
    CrossRef    
  9. Jabal, M.F.A.B., S. Hamid, S. Shuib and I. Ahmad, 2013. Leaf features extraction and recognition approaches to classify plant. J. Comput. Sci., 9(10): 1295-1304.
    CrossRef    
  10. Randen, T. and J.H. Husy, 1999. Filtering for texture classification: A comparative study. IEEE T. Pattern Anal., 21: 291-310.
    CrossRef    
  11. Wu, W.R. and S.C. Wei, 1996. Rotation and gray-scale transform-invariant texture classification using spiral resampling, subband decomposition and hidden Markov model. IEEE T. Image Process., 5(10): 1423-1434.
    CrossRef    PMid:18290060    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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