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

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

趙鳳^{1, 2, ,},
孫文靜^{1, 2},
劉漢強(qiáng)³,
曾哲^{1, 2}

1.
西安郵電大學(xué)通信與信息工程學(xué)院西安 710121
2.
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
3.
陜西師范大學(xué)計(jì)算機(jī)科學(xué)學(xué)院西安 710119

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
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

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

Feng ZHAO^{1, 2
, ,},
Wenjing SUN^{1, 2},
Hanqiang LIU³,
Zhe ZENG^{1, 2}

1.
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
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
3.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China

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

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.
- Image segmentation /
- Intuitionistic fuzzy clustering /
- Flower pollination optimization /
- Spatial information /
- Nearest neighbor learning

HTML全文

圖 1 各空間信息抗噪性能對(duì)比

下載: 全尺寸圖片幻燈片

圖 2 算法聚類準(zhǔn)確率各類型噪聲下隨α變化結(jié)果

下載: 全尺寸圖片幻燈片

圖 3 算法聚類準(zhǔn)確率各類型噪聲下隨β變化結(jié)果

下載: 全尺寸圖片幻燈片

圖 4 #241004的高斯噪聲圖像分割結(jié)果

下載: 全尺寸圖片幻燈片

圖 5 #241004的椒鹽噪聲圖像分割結(jié)果

下載: 全尺寸圖片幻燈片

圖 6 #241004的混合噪聲圖像分割結(jié)果

下載: 全尺寸圖片幻燈片

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

參考文獻(xiàn)(19)

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

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問(wèn)統(tǒng)計(jì)