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Color Image Segmentation Using Soft Rough Fuzzy-C-Means and Local Binary Pattern


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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


Pages

Total Pages: 10

DOI
10.31209/2019.100000121


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Published

Online Article

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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