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An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation

An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation

B. S. Harish, M. S. Maheshan, C. K. Roopa, S. V. Aruna Kumar
Copyright: © 2021 |Volume: 10 |Issue: 1 |Pages: 14
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781799861287|DOI: 10.4018/IJNCR.2021010101
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MLA

Harish, B. S., et al. "An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation." IJNCR vol.10, no.1 2021: pp.1-14. http://doi.org/10.4018/IJNCR.2021010101

APA

Harish, B. S., Maheshan, M. S., Roopa, C. K., & Kumar, S. V. (2021). An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation. International Journal of Natural Computing Research (IJNCR), 10(1), 1-14. http://doi.org/10.4018/IJNCR.2021010101

Chicago

Harish, B. S., et al. "An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation," International Journal of Natural Computing Research (IJNCR) 10, no.1: 1-14. http://doi.org/10.4018/IJNCR.2021010101

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Abstract

This article performs the sclera segmentation task by proposing a new hybrid symbolic fuzzy c-means (HSFCM) clustering method. Practically, though the data point exhibits some sort of similarity, unfortunately they are not undistinguishable and exhibit some sort of dissimilarity. Thus, to capture these disparities, the proposed work uses symbolic interval valued representation method. Further, to handle uncertainty and imprecision, the paper has proposed to use symbolic fuzzy clustering methods. To assess the performance of the proposed method, extensive experimentation is conducted on SSRBC2016 dataset. The proposed clustering method is compared with existing FCM, KFCM, RSKFCM method in terms of cluster validity indices and accuracy. The obtained outcomes demonstrated that the proposed method performed better compared to the contemporary methods.

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