Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation
Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segm...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Chengmao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: The visual computer - Berlin : Springer, 1985, 39(2021), 1 vom: 12. Nov., Seite 149-181 |
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| Übergeordnetes Werk: |
volume:39 ; year:2021 ; number:1 ; day:12 ; month:11 ; pages:149-181 |
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| DOI / URN: |
10.1007/s00371-021-02319-8 |
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SPR049018167 |
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| 520 | |a Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. | ||
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| 650 | 4 | |a Total Bregman divergence |7 (dpeaa)DE-He213 | |
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10.1007/s00371-021-02319-8 doi (DE-627)SPR049018167 (SPR)s00371-021-02319-8-e DE-627 ger DE-627 rakwb eng Wu, Chengmao verfasserin (orcid)0000-0002-5881-4723 aut Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. Image segmentation (dpeaa)DE-He213 Intuitionistic fuzzy clustering (dpeaa)DE-He213 Total Bregman divergence (dpeaa)DE-He213 Local similarity measure (dpeaa)DE-He213 Intuitionistic fuzzy weighted local information factor (dpeaa)DE-He213 Huang, Congcong (orcid)0000-0002-2971-8978 aut Zhang, Jiajia (orcid)0000-0002-3144-4179 aut Enthalten in The visual computer Berlin : Springer, 1985 39(2021), 1 vom: 12. Nov., Seite 149-181 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2021 number:1 day:12 month:11 pages:149-181 https://dx.doi.org/10.1007/s00371-021-02319-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2021 1 12 11 149-181 |
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10.1007/s00371-021-02319-8 doi (DE-627)SPR049018167 (SPR)s00371-021-02319-8-e DE-627 ger DE-627 rakwb eng Wu, Chengmao verfasserin (orcid)0000-0002-5881-4723 aut Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. Image segmentation (dpeaa)DE-He213 Intuitionistic fuzzy clustering (dpeaa)DE-He213 Total Bregman divergence (dpeaa)DE-He213 Local similarity measure (dpeaa)DE-He213 Intuitionistic fuzzy weighted local information factor (dpeaa)DE-He213 Huang, Congcong (orcid)0000-0002-2971-8978 aut Zhang, Jiajia (orcid)0000-0002-3144-4179 aut Enthalten in The visual computer Berlin : Springer, 1985 39(2021), 1 vom: 12. Nov., Seite 149-181 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2021 number:1 day:12 month:11 pages:149-181 https://dx.doi.org/10.1007/s00371-021-02319-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2021 1 12 11 149-181 |
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10.1007/s00371-021-02319-8 doi (DE-627)SPR049018167 (SPR)s00371-021-02319-8-e DE-627 ger DE-627 rakwb eng Wu, Chengmao verfasserin (orcid)0000-0002-5881-4723 aut Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. Image segmentation (dpeaa)DE-He213 Intuitionistic fuzzy clustering (dpeaa)DE-He213 Total Bregman divergence (dpeaa)DE-He213 Local similarity measure (dpeaa)DE-He213 Intuitionistic fuzzy weighted local information factor (dpeaa)DE-He213 Huang, Congcong (orcid)0000-0002-2971-8978 aut Zhang, Jiajia (orcid)0000-0002-3144-4179 aut Enthalten in The visual computer Berlin : Springer, 1985 39(2021), 1 vom: 12. Nov., Seite 149-181 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2021 number:1 day:12 month:11 pages:149-181 https://dx.doi.org/10.1007/s00371-021-02319-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2021 1 12 11 149-181 |
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10.1007/s00371-021-02319-8 doi (DE-627)SPR049018167 (SPR)s00371-021-02319-8-e DE-627 ger DE-627 rakwb eng Wu, Chengmao verfasserin (orcid)0000-0002-5881-4723 aut Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. Image segmentation (dpeaa)DE-He213 Intuitionistic fuzzy clustering (dpeaa)DE-He213 Total Bregman divergence (dpeaa)DE-He213 Local similarity measure (dpeaa)DE-He213 Intuitionistic fuzzy weighted local information factor (dpeaa)DE-He213 Huang, Congcong (orcid)0000-0002-2971-8978 aut Zhang, Jiajia (orcid)0000-0002-3144-4179 aut Enthalten in The visual computer Berlin : Springer, 1985 39(2021), 1 vom: 12. Nov., Seite 149-181 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2021 number:1 day:12 month:11 pages:149-181 https://dx.doi.org/10.1007/s00371-021-02319-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2021 1 12 11 149-181 |
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10.1007/s00371-021-02319-8 doi (DE-627)SPR049018167 (SPR)s00371-021-02319-8-e DE-627 ger DE-627 rakwb eng Wu, Chengmao verfasserin (orcid)0000-0002-5881-4723 aut Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. Image segmentation (dpeaa)DE-He213 Intuitionistic fuzzy clustering (dpeaa)DE-He213 Total Bregman divergence (dpeaa)DE-He213 Local similarity measure (dpeaa)DE-He213 Intuitionistic fuzzy weighted local information factor (dpeaa)DE-He213 Huang, Congcong (orcid)0000-0002-2971-8978 aut Zhang, Jiajia (orcid)0000-0002-3144-4179 aut Enthalten in The visual computer Berlin : Springer, 1985 39(2021), 1 vom: 12. Nov., Seite 149-181 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2021 number:1 day:12 month:11 pages:149-181 https://dx.doi.org/10.1007/s00371-021-02319-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2021 1 12 11 149-181 |
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Enthalten in The visual computer 39(2021), 1 vom: 12. Nov., Seite 149-181 volume:39 year:2021 number:1 day:12 month:11 pages:149-181 |
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Enthalten in The visual computer 39(2021), 1 vom: 12. Nov., Seite 149-181 volume:39 year:2021 number:1 day:12 month:11 pages:149-181 |
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To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image segmentation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intuitionistic fuzzy clustering</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Total Bregman divergence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Local similarity measure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intuitionistic fuzzy weighted local information factor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Congcong</subfield><subfield code="0">(orcid)0000-0002-2971-8978</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Jiajia</subfield><subfield code="0">(orcid)0000-0002-3144-4179</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The visual computer</subfield><subfield code="d">Berlin : Springer, 1985</subfield><subfield code="g">39(2021), 1 vom: 12. 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|
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Wu, Chengmao |
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Wu, Chengmao misc Image segmentation misc Intuitionistic fuzzy clustering misc Total Bregman divergence misc Local similarity measure misc Intuitionistic fuzzy weighted local information factor Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation |
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Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation Image segmentation (dpeaa)DE-He213 Intuitionistic fuzzy clustering (dpeaa)DE-He213 Total Bregman divergence (dpeaa)DE-He213 Local similarity measure (dpeaa)DE-He213 Intuitionistic fuzzy weighted local information factor (dpeaa)DE-He213 |
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misc Image segmentation misc Intuitionistic fuzzy clustering misc Total Bregman divergence misc Local similarity measure misc Intuitionistic fuzzy weighted local information factor |
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misc Image segmentation misc Intuitionistic fuzzy clustering misc Total Bregman divergence misc Local similarity measure misc Intuitionistic fuzzy weighted local information factor |
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intuitionistic fuzzy information-driven total bregman divergence fuzzy clustering with multiple local information constraints for image segmentation |
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Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation |
| abstract |
Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstractGer |
Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| title_short |
Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation |
| url |
https://dx.doi.org/10.1007/s00371-021-02319-8 |
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| author2 |
Huang, Congcong Zhang, Jiajia |
| author2Str |
Huang, Congcong Zhang, Jiajia |
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| doi_str |
10.1007/s00371-021-02319-8 |
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2024-07-03T22:50:53.105Z |
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| score |
7.400198 |