Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.211526-211539
Main Authors: Jia, Xiaohong, Lei, Tao, Liu, Peng, Xue, Dinghua, Meng, Hongying, Nandi, Asoke K.
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Density
density peak (DP) algorithm
Eigenvalues
fuzzy clustering
graph clustering
Image reconstruction
Image segmentation
Merging
Morphology
Optimization
Reconstruction
Transforms
Tuning
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Summary:Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3039742