基于核空間的加權(quán)鄰域約束直覺模糊聚類算法

張潔玉; 李佐勇

doi:10.11999/JEIT161317

基于核空間的加權(quán)鄰域約束直覺模糊聚類算法

doi: 10.11999/JEIT161317

張潔玉¹,
李佐勇²

1.
(中國藥科大學(xué)理學(xué)院南京 211198) ②(閩江學(xué)院計算機(jī)科學(xué)系福州 350108) ③(福建省信息處理與智能控制重點(diǎn)實驗室福州 350108)

基金項目:

國家自然科學(xué)基金青年基金(61501522)，福州市科技計劃項目(2016-S-116)，福建省新世紀(jì)優(yōu)秀人才支持計劃(NCETFJ)，福建省高校青年自然基金重點(diǎn)項目(JZ160467)，福建省引導(dǎo)性項目(2017H0030)

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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-12-08
- 修回日期: 2017-04-18
- 刊出日期: 2017-09-19

Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy C-means

ZHANG Jieyu¹,
LI Zuoyong²

1.
(School of Science, China Pharmaceutical University, Nanjing 211198, China)

Funds:

The National Natural Science Foundation of China (61501522), Fuzhou Science and Technology Planning Project (2016-S-116), The Program for New Century Excellent Talents in Fujian Province University (NCETFJ), The Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467), The Fujian Provincial Leading Project (2017H0030)

摘要

摘要: 該文針對直覺模糊聚類算法不考慮空間鄰域信息的缺點(diǎn)，提出一種基于核空間和加權(quán)鄰域約束的直覺模糊C均值聚類算法。該算法首先在直覺模糊C均值(Intuitionistic Fuzzy C-Means, IFCM)算法的基礎(chǔ)上加入空間鄰域約束關(guān)系，且賦予鄰域內(nèi)每個點(diǎn)不同的權(quán)重；接著采用核誘導(dǎo)函數(shù)代替歐氏距離計算各點(diǎn)到聚類中心的距離；然后創(chuàng)建包含鄰域信息的新的目標(biāo)函數(shù)，最優(yōu)化該目標(biāo)函數(shù)得到新的隸屬度及聚類中心的迭代表達(dá)式。利用所提出的新算法與同類聚類算法及基于顯著過渡區(qū)域的二值化算法進(jìn)行圖像分割，并對結(jié)果進(jìn)行定量分析后可知，所提出的算法最高能夠得到0.9776的F度量值。實驗結(jié)果表明新算法性能穩(wěn)定并且具有較高的分割精度。
- 模糊C均值聚類 /
- 直覺模糊集 /
- 鄰域信息約束 /
- 核空間
Abstract: To overcome the shortcoming of Intuitionistic Fuzzy C-Means (IFCM) that it does not take into account the spatial information, a new Kernel-based algorithm with Weighted Spatial Information (KWSI_IFCM) is proposed. Firstly, the constraint of weighted spatial neighborhood information is added. Secondly, instead of Euclidean distance, kernel-induced function is used to measure the distance between pixels and cluster centers. Thirdly, a new clustering objective function is created and then the iterative expressions of new membership and clustering centers are obtained by optimizing the new function. The quantitative analysis of image segmentation results using the new algorithm, other similar methods and a binarization method based on salient transition region shows that the new algorithm can get the F-measure value with 0.9776. The experimental results demonstrate that the proposed algorithm can obtain higher stability and segmentation accuracy than similar fuzzy C-mean algorithm.
- Fuzzy C-Means clustering /
- Intuitionistic fuzzy set /
- Spatial neighborhood information constraints /
- Kernel space

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