@article{MAKHILLJEAS20149513437,
    title = {Learning Improved Circular Difference and Statistical Directional Patterns for Texture Classifiaction},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {9},
    number = {5},
    pages = {147-152},
    year = {2014},
    issn = {1816-949x},
    doi = {jeasci.2014.147.152},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2014.147.152},
    author = {Randa Boukhris,Alima,Masmoudi and},
    keywords = {Texture analysis,ICDSDP,LBP,outex database,curetgrey database,Chi-square distance},
    abstract = {Thanks to its simplicity and computational efficiency, Local 
  Binary Pattern (LBP) has been widely utilized in texture classification. Traditional 
  LBP codes the local difference. It also, uses the binary code histogram to model 
  a given image. However, the directional statistical information is not taken 
  into consideration in LBP. In this study, researchers present the Improved Circular 
  Difference and Statistical Directional Patterns (ICDSDP). It is a new textual 
  approach for texture classification accuracy. It is a combination of the circular 
  difference of the directional information with oriented standard deviation. 
  This approach aims at improving the texture classification. Experiments done 
  on Outex and Curetgrey, large texture databases have shown that the application 
  of the proposed texture feature extraction and classification approach can significantly 
  ameliorate the classification accuracy of LBP. Compared to other methods, the 
  proposed scheme could remarkably improve the classification accuracy. It could 
  also, reduce classification.}
    }