Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/18966
Title: Automatic Fuzzy Clustering Framework for Image Segmentation
Authors: Lei, T
Liu, P
Jia, X
Zhang, X
Meng, H
Nandi, AK
Keywords: Fuzzy clustering;Image segmentation;Superpixel;Density peak (DP) algorithm
Issue Date: 23-Jul-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Lei, T. et al. (2020) 'Automatic Fuzzy Clustering Framework for Image Segmentation', IEEE Transactions on Fuzzy Systems, 28 (9), pp. 2078 - 2092. doi: 10.1109/tfuzz.2019.2930030,
Abstract: Copyright © 2019 The Authors. 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 the issue, here we proposed an automatic fuzzy clustering framework (AFCF) for image segmentation. The proposed framework has threefold contributions. Firstly, 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. Secondly, 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.
URI: https://bura.brunel.ac.uk/handle/2438/18966
DOI: https://doi.org/10.1109/TFUZZ.2019.2930030
ISSN: 1063-6706
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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