Unsupervised fuzzy model-based image segmentation
•A fuzzy model-based segmentation model with neighboring information is developed.•Mathematical analysis of the segmentation model is performed.•An unsupervised fuzzy model-based image segmentation algorithm is proposed. This paper presents a novel unsupervised fuzzy model-based image segmentation a...
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| Vydané v: | Signal processing Ročník 171; s. 107483 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.06.2020
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| ISSN: | 0165-1684, 1872-7557 |
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| Abstract | •A fuzzy model-based segmentation model with neighboring information is developed.•Mathematical analysis of the segmentation model is performed.•An unsupervised fuzzy model-based image segmentation algorithm is proposed.
This paper presents a novel unsupervised fuzzy model-based image segmentation algorithm. The proposed algorithm integrates color and generalized Gaussian density (GGD) into the fuzzy clustering algorithm and incorporates their neighboring information into the learning process to improve the segmentation accuracy. In addition, a membership entropy term is used to make the algorithm not sensitive to initial clusters. To optimize the objective function of the proposed segmentation model, we define the dissimilarity measure between GGD models using the Kullback–Leibler divergence, which evaluates their discrepancy in the space of generalized probability distributions via only the model parameters. We also present mathematical analysis that proves the existence of the cluster center for the GGD parameters, thus establishing a theoretical basis for its use. Experimental results show that our proposed method has a promising performance compared with the current state-of-the-art fuzzy clustering-based approaches. |
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| AbstractList | •A fuzzy model-based segmentation model with neighboring information is developed.•Mathematical analysis of the segmentation model is performed.•An unsupervised fuzzy model-based image segmentation algorithm is proposed.
This paper presents a novel unsupervised fuzzy model-based image segmentation algorithm. The proposed algorithm integrates color and generalized Gaussian density (GGD) into the fuzzy clustering algorithm and incorporates their neighboring information into the learning process to improve the segmentation accuracy. In addition, a membership entropy term is used to make the algorithm not sensitive to initial clusters. To optimize the objective function of the proposed segmentation model, we define the dissimilarity measure between GGD models using the Kullback–Leibler divergence, which evaluates their discrepancy in the space of generalized probability distributions via only the model parameters. We also present mathematical analysis that proves the existence of the cluster center for the GGD parameters, thus establishing a theoretical basis for its use. Experimental results show that our proposed method has a promising performance compared with the current state-of-the-art fuzzy clustering-based approaches. |
| ArticleNumber | 107483 |
| Author | Yu, Carisa Ng, Tsz Ching Choy, Siu Kai |
| Author_xml | – sequence: 1 givenname: Siu Kai orcidid: 0000-0002-7859-0202 surname: Choy fullname: Choy, Siu Kai email: skchoy@hsu.edu.hk – sequence: 2 givenname: Tsz Ching surname: Ng fullname: Ng, Tsz Ching email: tcng@hsu.edu.hk – sequence: 3 givenname: Carisa surname: Yu fullname: Yu, Carisa email: carisayu@hsu.edu.hk |
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| CitedBy_id | crossref_primary_10_1016_j_patrec_2022_09_019 crossref_primary_10_3390_biomedicines9091222 crossref_primary_10_1007_s10489_022_03690_2 crossref_primary_10_1145_3476514 crossref_primary_10_1007_s42001_024_00315_1 crossref_primary_10_3390_s23146612 crossref_primary_10_1016_j_apm_2020_09_008 crossref_primary_10_1016_j_engappai_2025_111092 crossref_primary_10_1016_j_jvcir_2021_103306 crossref_primary_10_1016_j_sigpro_2021_108293 crossref_primary_10_13005_bpj_3056 crossref_primary_10_1016_j_eswa_2022_117019 crossref_primary_10_3390_mi12121478 crossref_primary_10_1080_01431161_2023_2275326 crossref_primary_10_1049_ipr2_12896 crossref_primary_10_1080_07038992_2024_2418091 crossref_primary_10_1016_j_asoc_2021_108005 crossref_primary_10_3233_JIFS_211093 crossref_primary_10_3390_math9192392 crossref_primary_10_1007_s13369_021_06139_9 |
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