Parallel hesitant fuzzy C-means algorithm to image segmentation

Hesitant fuzzy information allows clustering data with multiple possible membership values for a single item in a reference set. Hesitant fuzzy sets have been applied in many decision-making problems, obtaining better results against others kinds of fuzzy sets. So, in this paper a method for image s...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2022-02, Vol.16 (1), p.73-81
Main Authors: Vela-Rincón, Virna V., Mújica-Vargas, Dante, de Jesus Rubio, Jose
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Computer Imaging
Computer Science
Decision making
Fuzzy set theory
Fuzzy sets
Image Processing and Computer Vision
Image segmentation
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
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Summary:Hesitant fuzzy information allows clustering data with multiple possible membership values for a single item in a reference set. Hesitant fuzzy sets have been applied in many decision-making problems, obtaining better results against others kinds of fuzzy sets. So, in this paper a method for image segmentation based on the hesitant fuzzy set theory is investigated. Additionally, processing time is sped up with a hardware-level parallelization technique using OpenMP. Comparing the experimental results, it can be seen that the segmentation by the propose algorithm is superior, compared to some of the state of the art. The most striking feature to emerge from this algorithm is its ability to preserve the details of the boundaries of the region, in addition to the fact that the regions are more homogeneous.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-021-01957-8