一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級(jí)搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問(wèn)題, 您可以本頁(yè)添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號(hào)碼
標(biāo)題
留言內(nèi)容
驗(yàn)證碼

基于近鄰搜索花授粉優(yōu)化的直覺(jué)模糊聚類圖像分割

趙鳳 孫文靜 劉漢強(qiáng) 曾哲

趙鳳, 孫文靜, 劉漢強(qiáng), 曾哲. 基于近鄰搜索花授粉優(yōu)化的直覺(jué)模糊聚類圖像分割[J]. 電子與信息學(xué)報(bào), 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428
引用本文: 趙鳳, 孫文靜, 劉漢強(qiáng), 曾哲. 基于近鄰搜索花授粉優(yōu)化的直覺(jué)模糊聚類圖像分割[J]. 電子與信息學(xué)報(bào), 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428
Feng ZHAO, Wenjing SUN, Hanqiang LIU, Zhe ZENG. Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428
Citation: Feng ZHAO, Wenjing SUN, Hanqiang LIU, Zhe ZENG. Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428

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
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61571361, 61671377, 61102095),西安郵電大學(xué)西郵新星團(tuán)隊(duì)基金(xyt2016-01)
詳細(xì)信息
    作者簡(jiǎn)介:

    趙鳳:女,1980年生,教授,研究方向?yàn)橛?jì)算智能與圖像處理

    孫文靜:女,1995年生,碩士生,研究方向?yàn)閳D像處理

    劉漢強(qiáng):男,1981年生,副教授,研究方向?yàn)槟J阶R(shí)別與圖像處理

    曾哲:男,1995年生,碩士生,研究方向?yàn)閳D像處理

    通訊作者:

    趙鳳 fzhao.xupt@gmail.com

  • 中圖分類號(hào): TP391

Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching

Funds: The National Natural Science Foundation of China (61571361, 61671377, 61102095), The New Star Team Foundation of Xi’an University of Posts & Telecommunications (xyt2016-01)
  • 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。

  • 圖  1  各空間信息抗噪性能對(duì)比

    圖  2  算法聚類準(zhǔn)確率各類型噪聲下隨α變化結(jié)果

    圖  3  算法聚類準(zhǔn)確率各類型噪聲下隨β變化結(jié)果

    圖  4  #241004的高斯噪聲圖像分割結(jié)果

    圖  5  #241004的椒鹽噪聲圖像分割結(jié)果

    圖  6  #241004的混合噪聲圖像分割結(jié)果

    表  1  各算法聚類準(zhǔn)確率對(duì)比

    圖像噪聲水平IFCMFPA-FCMFLICMIIFCMNDFCM本文算法
    高斯0.75360.73760.93060.76460.92790.9284
    #113016椒鹽0.82540.83200.90190.82680.91190.9290
    高斯&椒鹽0.78060.74430.91630.78060.90540.9175
    高斯0.83730.83570.90540.82340.89450.8986
    #101027椒鹽0.79620.79390.85860.80410.88570.8913
    高斯&椒鹽0.78060.78090.88340.77820.88390.8964
    高斯0.56400.56690.91010.56400.91120.8979
    #241004椒鹽0.67250.67250.64620.67250.86620.9116
    高斯&椒鹽0.53830.48470.64870.54420.84080.9012
    高斯0.83460.78880.93290.85700.93230.9332
    #15088椒鹽0.84160.83950.93210.84210.93060.9331
    高斯&椒鹽0.82250.79890.93260.82630.92850.9329
    高斯0.77190.83290.88830.63600.88060.8962
    #296059椒鹽0.75000.4822083190.66710.86540.9022
    高斯&椒鹽0.69750.27140.85300.60780.85820.8938
    下載: 導(dǎo)出CSV
  • 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:0372-2112.2004.03.016

    Lü 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
  • 加載中
圖(6) / 表(1)
計(jì)量
  • 文章訪問(wèn)數(shù):  2463
  • HTML全文瀏覽量:  953
  • PDF下載量:  128
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2019-06-11
  • 修回日期:  2019-12-09
  • 網(wǎng)絡(luò)出版日期:  2019-12-20
  • 刊出日期:  2020-06-04

目錄

    /

    返回文章
    返回