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DC Field | Value | Language |
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dc.contributor.author | Jia, X | - |
dc.contributor.author | Lei, T | - |
dc.contributor.author | Liu, P | - |
dc.contributor.author | Xue, D | - |
dc.contributor.author | Meng, H | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2020-12-07T03:53:59Z | - |
dc.date.available | 2020-12-07T03:53:59Z | - |
dc.date.issued | 2020-11-26 | - |
dc.identifier.citation | Jia, X. et al. (2020) 'Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering', IEEE Access, 8, pp. 211526 - 211539. doi: 10.1109/access.2020.3039742. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/21964 | - |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61871259 and 61811530325); 10.13039/501100000288-IECnNSFCn170396, Royal Society, U.K. (Grant Number: 61871260, 61672333 and 61861024); Science and Technology Program of Shaanxi Province (Grant Number: 2020NY-172). | - |
dc.format.extent | 211526 - 211539 | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | - |
dc.rights | Copyright The Author(s) 2020. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Image segmentation | en_US |
dc.subject | Fuzzy clustering | en_US |
dc.subject | Graph clustering | en_US |
dc.subject | Density peak (DP) algorithm | en_US |
dc.title | Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/access.2020.3039742 | - |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Published | - |
pubs.volume | 8 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright The Author(s) 2020. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | 4.07 MB | Adobe PDF | View/Open |
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