Open Access
ARTICLE
Color Image Segmentation Using Soft Rough Fuzzy-C-Means and Local Binary Pattern
R.V.V. Krishna1,*, S. Srinivas Kumar2
1 Department of ECE, Aditya College of Engineering & Technology, Surampalem, Kakinada, India
2 Department of ECE, JNT University, Kakinada, Andhra Pradesh, India
* Corresponding Author: R.V.V. Krishna,
Intelligent Automation & Soft Computing 2020, 26(2), 281-290. https://doi.org/10.31209/2019.100000121
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.
Keywords
Cite This Article
APA Style
Krishna, R., Kumar, S.S. (2020). Color image segmentation using soft rough fuzzy-c-means and local binary pattern. Intelligent Automation & Soft Computing, 26(2), 281-290. https://doi.org/10.31209/2019.100000121
Vancouver Style
Krishna R, Kumar SS. Color image segmentation using soft rough fuzzy-c-means and local binary pattern. Intell Automat Soft Comput . 2020;26(2):281-290 https://doi.org/10.31209/2019.100000121
IEEE Style
R. Krishna and S.S. Kumar, "Color Image Segmentation Using Soft Rough Fuzzy-C-Means and Local Binary Pattern," Intell. Automat. Soft Comput. , vol. 26, no. 2, pp. 281-290. 2020. https://doi.org/10.31209/2019.100000121