@Article{2019.100000121,
AUTHOR = {R.V.V. Krishna, S. Srinivas Kumar},
TITLE = {Color Image Segmentation Using Soft Rough Fuzzy-C-Means and Local  Binary Pattern},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {281--290},
URL = {http://www.techscience.com/iasc/v26n2/39943},
ISSN = {2326-005X},
ABSTRACT = {In this paper, a color image segmentation algorithm is proposed by extracting 
both texture and color features and applying them to the one -against-all multi 
class support vector machine (MSVM) classifier for segmentation. Local Binary 
Pattern is used for extracting the textural features and L*a*b color model is 
used for obtaining the color features. The MSVM is trained using the samples 
obtained from a novel soft rough fuzzy c-means (SRFCM) clustering. The fuzzy 
set based membership functions capably handle the problem of overlapping 
clusters. The lower and upper approximation concepts of rough sets deal well 
with uncertainty, vagueness, and incompleteness in data. Parameterization is 
not a prerequisite in defining soft set theory. The goodness aspects of soft sets, 
rough sets, and fuzzy sets are incorporated in the proposed algorithm to 
achieve improved segmentation performance. The local binary pattern (LBP) 
used for texture feature extraction has the advantage of being dealt in the 
spatial domain thereby reducing computational complexity.},
DOI = {10.31209/2019.100000121}
}