@article{MAKHILLRJAS2012759211,
    title = {Adaptive Color Texture Image Segmentation Using α-Cut Implemented Interval Type-2 Fuzzy C-Means},
    journal = {Research Journal of Applied Sciences},
    volume = {7},
    number = {5},
    pages = {258-265},
    year = {2012},
    issn = {1815-932x},
    doi = {rjasci.2012.258.265},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2012.258.265},
    author = {P. and},
    keywords = {Type-2 fuzzy,color texture segmentation,IT2 fuzzy,ICICM,CCM,India},
    abstract = {Texture segmentation is the process of partitioning an image 
  into regions with different textures containing similar group of pixels. The 
  color and texture information collectively has strong link with the human perception. 
  So many applications need to combine color and texture features analyse the 
  image content accurately. This study presents an unsupervised color texture 
  image segmentation method which is based on the feature extraction and fuzzy 
  clustering. The proposed method includes color texture segmentation using Haralick 
  features extracted from Integrated Color and Intensity Co-occurrence Matrix 
  (ICICM). Then, α-cut implemented interval type-2 fuzzy c-mean clustering 
  algorithm is utilized to cluster the obtained feature vectors into several classes 
  corresponding to different regions of the textured image. Experimental result 
  shows that the proposed hybrid approach could obtain better cluster quality 
  and segmentation results compared to state of art image segmentation algorithms.}
    }