Automatic Fuzzy Clustering Framework for Image Segmentation

Clustering algorithms by minimizing an objective function share a clear drawback of having to set the number of clusters manually. Although density peak clustering is able to find the number of clusters, it suffers from memory overflow when it is used for image segmentation because a moderate-size i...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 28; no. 9; pp. 2078 - 2092
Main Authors: Lei, Tao, Liu, Peng, Jia, Xiaohong, Zhang, Xuande, Meng, Hongying, Nandi, Asoke K.
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Clustering
Clustering algorithms
Color
Computational complexity
Density
Density peak (DP) algorithm
Entropy
fuzzy clustering
Histograms
Image segmentation
Linear programming
Pixels
Similarity
superpixel
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:Clustering algorithms by minimizing an objective function share a clear drawback of having to set the number of clusters manually. Although density peak clustering is able to find the number of clusters, it suffers from memory overflow when it is used for image segmentation because a moderate-size image usually includes a large number of pixels leading to a huge similarity matrix. To address this issue, here we proposed an automatic fuzzy clustering framework (AFCF) for image segmentation. The proposed framework has threefold contributions. First, the idea of superpixel is used for the density peak (DP) algorithm, which efficiently reduces the size of the similarity matrix and thus improves the computational efficiency of the DP algorithm. Second, we employ a density balance algorithm to obtain a robust decision-graph that helps the DP algorithm achieve fully automatic clustering. Finally, a fuzzy c-means clustering based on prior entropy is used in the framework to improve image segmentation results. Because the spatial neighboring information of both the pixels and membership are considered, the final segmentation result is improved effectively. Experiments show that the proposed framework not only achieves automatic image segmentation, but also provides better segmentation results than state-of-the-art algorithms.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2019.2930030