基于近鄰搜索花授粉優(yōu)化的直覺(jué)模糊聚類圖像分割
doi: 10.11999/JEIT190428
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西安郵電大學(xué)通信與信息工程學(xué)院 西安 710121
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西安郵電大學(xué)電子信息現(xiàn)場(chǎng)勘驗(yàn)應(yīng)用技術(shù)公安部重點(diǎn)實(shí)驗(yàn)室 西安 710121
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陜西師范大學(xué)計(jì)算機(jī)科學(xué)學(xué)院 西安 710119
Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching
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School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
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Key Laboratory of Electronic Information Application Technology for Scene Investigation of Ministry of Public Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
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School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
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摘要:
為克服傳統(tǒng)模糊聚類算法應(yīng)用于圖像分割時(shí),易受噪聲影響,對(duì)聚類中心初始值敏感,易陷入局部最優(yōu),模糊信息處理能力不足等缺陷,該文提出基于近鄰搜索花授粉優(yōu)化的直覺(jué)模糊聚類圖像分割算法。首先設(shè)計(jì)一種新穎的圖像空間信息提取策略,進(jìn)而構(gòu)造融合圖像空間信息的直覺(jué)模糊聚類目標(biāo)函數(shù),提高對(duì)于噪聲的魯棒性,提升算法處理圖像中模糊信息的能力。為了優(yōu)化上述目標(biāo)函數(shù),提出一種基于近鄰學(xué)習(xí)搜索機(jī)制的花授粉算法,實(shí)現(xiàn)對(duì)于聚類中心的尋優(yōu),解決對(duì)于聚類中心初始值敏感,易陷入局部最優(yōu)的問(wèn)題。實(shí)驗(yàn)結(jié)果表明所提算法能在多種噪聲圖像上取得令人滿意的分割效果。
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關(guān)鍵詞:
- 圖像分割 /
- 直覺(jué)模糊聚類 /
- 花授粉優(yōu)化 /
- 空間信息 /
- 近鄰學(xué)習(xí)
Abstract:In order to overcome shortcomings of the traditional fuzzy clustering algorithm for image segmentation, such as that are easily affected by noise, sensitive to the initial value of clustering center, easily falling into local optimum, and inadequate ability of fuzzy information processing, an intuitionistic fuzzy clustering image segmentation algorithm is proposed based on flower pollination optimization with nearest neighbor searching. Firstly, a novel extraction strategy of image spatial information is proposed, and then an intuitionistic fuzzy clustering objective function with image spatial information is constructed to improve the algorithm’s robustness against noise and enhance the ability of the algorithm to process the image fuzzy information. In order to overcome the defects of sensitivity to clustering centers and easily falling into local optimum, a flower pollination algorithm based on nearest neighbor learning search mechanism is proposed. Experimental results show that the proposed method can get satisfactory segmentation results on a variety of noisy images.
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表 1 各算法聚類準(zhǔn)確率對(duì)比
圖像 噪聲水平 IFCM FPA-FCM FLICM IIFCM NDFCM 本文算法 高斯 0.7536 0.7376 0.9306 0.7646 0.9279 0.9284 #113016 椒鹽 0.8254 0.8320 0.9019 0.8268 0.9119 0.9290 高斯&椒鹽 0.7806 0.7443 0.9163 0.7806 0.9054 0.9175 高斯 0.8373 0.8357 0.9054 0.8234 0.8945 0.8986 #101027 椒鹽 0.7962 0.7939 0.8586 0.8041 0.8857 0.8913 高斯&椒鹽 0.7806 0.7809 0.8834 0.7782 0.8839 0.8964 高斯 0.5640 0.5669 0.9101 0.5640 0.9112 0.8979 #241004 椒鹽 0.6725 0.6725 0.6462 0.6725 0.8662 0.9116 高斯&椒鹽 0.5383 0.4847 0.6487 0.5442 0.8408 0.9012 高斯 0.8346 0.7888 0.9329 0.8570 0.9323 0.9332 #15088 椒鹽 0.8416 0.8395 0.9321 0.8421 0.9306 0.9331 高斯&椒鹽 0.8225 0.7989 0.9326 0.8263 0.9285 0.9329 高斯 0.7719 0.8329 0.8883 0.6360 0.8806 0.8962 #296059 椒鹽 0.7500 0.4822 08319 0.6671 0.8654 0.9022 高斯&椒鹽 0.6975 0.2714 0.8530 0.6078 0.8582 0.8938 下載: 導(dǎo)出CSV
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趙鳳. 基于模糊聚類的圖像分割[M]. 西安: 西安電子科技大學(xué)出版社, 2015: 1–5.ZHAO Feng. Fuzzy Clustering for Image Segmentation[M]. Xi’an: Xidian University Press, 2015: 1–5. GU Jing, JIAO Licheng, YANG Shuyuan, et al. Fuzzy double c-means clustering based on sparse self-representation[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(2): 612–626. doi: 10.1109/TFUZZ.2017.2686804 BEZDEK J C, EHRLICH R, and FULL W. FCM: The fuzzy c-means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2/3): 191–203. doi: 10.1016/0098-3004(84)90020-7 KRINIDIS S and CHATZIS V. A robust fuzzy local information c-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1328–1337. doi: 10.1109/TIP.2010.2040763 GUO Fangfang, WANG Xiuxiu, and SHEN Jie. Adaptive fuzzy c-means algorithm based on local noise detecting for image segmentation[J]. IET Image Processing, 2016, 10(4): 272–279. doi: 10.1049/iet-ipr.2015.0236 LI M Q, XU L P, XU Na, et al. SAR image segmentation based on improved grey wolf optimization algorithm and fuzzy c-means[J]. Mathematical Problems in Engineering, 2018: 4576015. doi: 10.1155/2018/4576015 YANG Xinshe. Flower pollination algorithm for global optimization[C]. The 11th International Conference on Unconventional Computing and Natural Computation, Orléan, France, 2012: 240–249. doi: 10.1007/978-3-642-32894-7_27. WANG Rui, ZHOU Yongquan, QIAO Shilei, et al. Flower pollination algorithm with bee pollinator for cluster analysis[J]. Information Processing Letters, 2016, 116(1): 1–14. doi: 10.1016/j.ipl.2015.08.007 ALYASSERI Z A A, KHADER A T, AL-BETAR M A, et al. Variants of the Flower Pollination Algorithm: A Review[M]. YANG Xinshe. Nature-Inspired Algorithms and Applied Optimization. Cham: Springer, 2018: 91–118. doi: 10.1007/978-3-319-67669-2_5. KOWALSKI P A, ŁUKASIK S, CHARYTANOWICZ M, et al. Nature Inspired Clustering-use Cases of Krill Herd Algorithm and Flower Pollination Algorithm[M]. KÓCZY L T, MEDINA-MORENO J, and RAMÍREZ-POUSSA E. Interactions between Computational Intelligence and Mathematics Part 2. Cham: Springer, 2019: 83–98. doi: 10.1007/978-3-030-01632-6_6. CUI Weijia and HE Yuzhu. Biological flower pollination algorithm with orthogonal learning strategy and catfish effect mechanism for global optimization problems[J]. Mathematical Problems in Engineering, 2018: 6906295. doi: 10.1155/2018/6906295 ATANASSOV K and GARGOV G. Interval valued intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems, 1989, 31(3): 343–349. doi: 10.1016/0165-0114(89)90205-4 CHAIRA T. A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images[J]. Applied Soft Computing, 2011, 11(2): 1711–1717. doi: 10.1016/j.asoc.2010.05.005 VERMA H, AGRAWAL R K, and SHARAN A. An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation[J]. Applied Soft Computing, 2016, 46: 543–557. doi: 10.1016/j.asoc.2015.12.022 YAGER R R. On the measure of fuzziness and negation. II. Lattices[J]. Information and Control, 1980, 44(3): 236–260. doi: 10.1016/S0019-9958(80)90156-4 WOODS R E and GONZALEZ R C. Real-time digital image enhancement[J]. Proceedings of the IEEE, 1981, 69(5): 643–654. doi: 10.1109/PROC.1981.12031 HUYNH-THU Q and GHANBARI M. The accuracy of PSNR in predicting video quality for different video scenes and frame rates[J]. Telecommunication Systems, 2012, 49(1): 35–48. doi: 10.1007/s11235-010-9351-x ALTMAN N S. An introduction to kernel and nearest-neighbor nonparametric regression[J]. The American Statistician, 1992, 46(3): 175–185. 呂振肅, 侯志榮. 自適應(yīng)變異的粒子群優(yōu)化算法[J]. 電子學(xué)報(bào), 2004, 32(3): 416–420. doi: 10.3321/j.issn:0372-2112.2004.03.016Lü Zhensu and HOU Zhirong. Particle swarm optimization with adaptive mutation[J]. Acta Electronica Sinica, 2004, 32(3): 416–420. doi: 10.3321/j.issn:0372-2112.2004.03.016 -