[1]朱占龙,马艳玲,董建彬,等.融入类贡献抑制因子的灰度级模糊C均值图像分割[J].智能系统学报,2021,16(4):641-648.[doi:10.11992/tis.202009019]
ZHU Zhanlong,MA Yanling,DONG Jianbin,et al.Gray level-based fuzzy C-means algorithm for image segmentation with inhibitors of cluster contribution[J].CAAI Transactions on Intelligent Systems,2021,16(4):641-648.[doi:10.11992/tis.202009019]
点击复制
ZHU Zhanlong,MA Yanling,DONG Jianbin,et al.Gray level-based fuzzy C-means algorithm for image segmentation with inhibitors of cluster contribution[J].CAAI Transactions on Intelligent Systems,2021,16(4):641-648.[doi:10.11992/tis.202009019]
融入类贡献抑制因子的灰度级模糊C均值图像分割
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第4期
页码:
641-648
栏目:
学术论文—机器学习
出版日期:
2021-07-05
- Title:
- Gray level-based fuzzy C-means algorithm for image segmentation with inhibitors of cluster contribution
- Keywords:
- fuzzy C-means algorithm; image segmentation; gray levels; spatial information; non-destructive testing image; denoising; cluster center; objective function
- 分类号:
- TP391
- 摘要:
- 基于灰度级模糊C均值图像分割算法具有分割速度快的优势。由于无损检测图像中背景类和目标类差异较大，该算法不能有效地将目标分割出来，故提出改进的基于灰度级的模糊C均值算法。构建了一种与类大小反向相关的类贡献抑制因子表达式，将之融入目标函数后能够降低较大类对目标函数的贡献，这可避免较小类的聚类中心受较大类的影响而靠近较大类的聚类中心。最小化新的目标函数可得新形式的隶属度和聚类中心表征形式。采用类大小差异较大的无损检测图像进行试验，结果显示本文算法得到的分割图像视觉效果良好，而且指标G_mean也更高，进一步提升了基于灰度级模糊C均值算法适应能力。
- Abstract:
- The fuzzy C-means algorithm for image segmentation based on the gray level has the advantage of fast segmentation speed. However, this algorithm cannot effectively segment object pixels from a nondestructive testing (NDT) image because of the great difference between the background and the object region. Therefore, an improved fuzzy C-means algorithm based on gray level is proposed. First, an expression called inhibitors of cluster contribution, which is inversely related to the class size, is constructed. Incorporating this expression into the objective function reduces the contribution of the larger cluster to the objective function, which then avoids the influence of the larger cluster on the cluster center of the smaller cluster. Second, a new form of membership degree and cluster center representation can be obtained by minimizing the new objective function. Lastly, NDT images with a large difference in cluster size are used for testing. Results show that the segmentation image obtained by this algorithm has a better visual effect and the index G_mean is higher, thereby further improving the adaptability of the fuzzy C-means algorithm based on gray level.
- 参考文献/References:
-
[1] 李玉, 胡海峰, 赵雪梅, 等. 遥感图像扫描聚类分割算法[J]. 信号处理, 2018, 34(9): 1130-1141
LI Yu, HU Haifeng, ZHAO Xuemei, et al. The remote sensing image scan clustering segmentation algorithm[J]. Journal of signal processing, 2018, 34(9): 1130-1141
[2] WU Chengmao, YANG Xiaoqiang. Robust credibilistic fuzzy local information clustering with spatial information constraints[J]. Digital signal processing, 2020, 97: 102615.
[3] 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.
[4] 丁晓峰, 何凯霖. 一种基于一致性分片FCM的图像分割算法[J]. 重庆邮电大学学报(自然科学版), 2017, 29(3): 377-381
DING Xiaofeng, HE Kailin. A homogeneous pieces based FCM algorithm for image segmentation[J]. Journal of Chongqing University of Posts and Telecommunications (natural science edition), 2017, 29(3): 377-381
[5] 王小鹏, 张永芳, 王伟, 等. 基于自适应滤波的快速广义模糊C均值聚类图像分割[J]. 模式识别与人工智能, 2018, 31(11): 1040-1046
WANG Xiaopeng, ZHANG Yongfang, WANG Wei, et al. Image segmentation using fast generalized fuzzy C-means clustering based on adaptive filtering[J]. Pattern recognition and artificial intelligence, 2018, 31(11): 1040-1046
[6] ZHAO Feng, FAN Jiulun, LIU Hanqiang. Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation[J]. Expert systems with applications, 2014, 41(9): 4083-4093.
[7] GUO Fangfang, WANG Xiuxiu, SHEN Jie. Adaptive fuzzy c-means algorithm based on local noise detecting for image segmentation[J]. IET image processing, 2016, 10(4): 272-279.
[8] 雷涛, 张肖, 加小红, 等. 基于模糊聚类的图像分割研究进展[J]. 电子学报, 2019, 47(8): 1776-1791
LEI Tao, ZHANG Xiao, JIA Xiaohong, et al. Research Progress on Image Segmentation Based on Fuzzy Clustering[J]. Acta electronica sinica, 2019, 47(8): 1776-1791
[9] FAN Jiulun, ZHEN Wenzhi, XIE Weixin. Suppressed fuzzy c-means clustering algorithm[J]. Pattern recognition letters, 2003, 24(9/10): 1607-1612.
[10] HUNG W L, YANG M S, CHEN Dehua. Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation[J]. Pattern recognition letters, 2006, 27(5): 424-438.
[11] SZILáGYI L, BENYó Z, SZILáGYI S M, et al. MR brain image segmentation using an enhanced fuzzy c-means algorithm[C]//Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Cancun, Mexico, 2003: 724-726.
[12] CAI Weiling, CHEN Songcan, ZHANG Daoqiang. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern recognition, 2007, 40(3): 825-838.
[13] 朱占龙, 王军芬. 基于自适应模糊C均值与后处理的图像分割算法[J]. 激光与光电子学进展, 2018, 55(1): 011004
ZHU Zhanlong, WANG Junfen. Image segmentation based on adaptive fuzzy c-means and post processing correction[J]. Laser & optoelectronics progress, 2018, 55(1): 011004
[14] 李喜孟. 无损检测[M]. 北京: 机械工业出版社, 2011.
[15] 侯旺, 钟立军, 张小虎, 等. 红外目标分割方法研究[J]. 国防科技大学学报, 2013, 35(2): 173-178
HOU Wang, ZHONG Lijun, ZHANG Xiaohu, et al. Research on infrared target segmentation[J]. Journal of National University of Defense Technology, 2013, 35(2): 173-178
[16] LIU Yun, HOU Tao, LIU Fu. Improving fuzzy c-means method for unbalanced dataset[J]. Electronics letters, 2015, 51(23): 1880-1882.
[17] LIN P L, HUANG P W, KUO C H, et al. A size-insensitive integrity-based fuzzy c-means method for data clustering[J]. Pattern recognition, 2014, 47(5): 2042-2056.
[18] 陆海青, 葛洪伟. 自适应灰度加权的鲁棒模糊C均值图像分割[J]. 智能系统学报, 2018, 13(4): 584-593
LU Haiqing, GE Hongwei. Adaptive gray-weighted robust fuzzy C-means algorithm for image segmentation[J]. CAAI transactions on intelligent systems, 2018, 13(4): 584-593
[19] SU C T, CHEN Longsheng, YIH Y. Knowledge acquisition through information granulation for imbalanced data[J]. Expert systems with applications, 2006, 31(3): 531-541.
- 相似文献/References:
-
[1]王科俊,郭庆昌.基于粒子群优化算法和改进的Snake模型的图像分割算法[J].智能系统学报,2007,2(1):53.
WANG Ke-jun,GUO Qing-chang.Image segmentation algorithm based on the PSO and improved Snake model[J].CAAI Transactions on Intelligent Systems,2007,2():53.
[2]陈小波,程显毅.一种基于MAS的自适应图像分割方法[J].智能系统学报,2007,2(4):80.
CHEN Xiao-bo,CHENG Xian-yi.An adaptive image segmentation technique based on multiAgent system[J].CAAI Transactions on Intelligent Systems,2007,2():80.
[3]刘咏梅,代丽洁.基于空间位置约束的K均值图像分割[J].智能系统学报,2010,5(1):67.
LIU Yong-mei,DAI Li-jie.An improved method of Kmeans image segmentation based on spatial position information[J].CAAI Transactions on Intelligent Systems,2010,5():67.
[4]吴一全,纪守新.灰度熵和混沌粒子群的图像多阈值选取[J].智能系统学报,2010,5(6):522.
WU Yi-quan,JI Shou-xin.Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2010,5():522.
[5]尚倩,阮秋琦,李小利.双目立体视觉的目标识别与定位[J].智能系统学报,2011,6(4):303.
SHANG Qian,RUAN Qiuqi,LI Xiaoli.Target recognition and location based on binocular stereo vision[J].CAAI Transactions on Intelligent Systems,2011,6():303.
[6]胡光龙,秦世引.动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J].智能系统学报,2012,7(1):61.
HU Guanglong,QIN Shiyin.High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J].CAAI Transactions on Intelligent Systems,2012,7():61.
[7]马慧,王科俊.采用旋转校正的指静脉图像感兴趣区域提取方法[J].智能系统学报,2012,7(3):230.
MA Hui,WANG Kejun.A region of interest extraction method using rotation rectified finger vein images[J].CAAI Transactions on Intelligent Systems,2012,7():230.
[8]尹雨山,王李进,尹义龙,等.回溯搜索优化算法辅助的多阈值图像分割[J].智能系统学报,2015,10(1):68.[doi:10.3969/j.issn.1673-4785.201410008]
YIN Yushan,WANG Lijin,YIN Yilong,et al.Backtracking search optimization algorithm assisted multilevel threshold for image segmentation[J].CAAI Transactions on Intelligent Systems,2015,10():68.[doi:10.3969/j.issn.1673-4785.201410008]
[9]吴一全,王凯,曹鹏祥.蜂群优化的二维非对称Tsallis交叉熵图像阈值选取[J].智能系统学报,2015,10(1):103.[doi:10.3969/j.issn.1673-4785.201403040]
WU Yiquan,WANG Kai,CAO Pengxiang.Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization[J].CAAI Transactions on Intelligent Systems,2015,10():103.[doi:10.3969/j.issn.1673-4785.201403040]
[10]张永库,尹灵雪,孙劲光.基于改进的遗传算法的模糊聚类算法[J].智能系统学报,2015,10(4):627.[doi:10.3969/j.issn.1673-4785.201503033]
ZHANG Yongku,YIN Lingxue,SUN Jinguang.Fuzzy clustering algorithm based on the improved genetic algorithm[J].CAAI Transactions on Intelligent Systems,2015,10():627.[doi:10.3969/j.issn.1673-4785.201503033]
备注/Memo
收稿日期:2020-09-15。
基金项目:河北省高等学校科学技术研究项目(QN2020263)
作者简介:朱占龙，博士，讲师，主要研究方向为图像处理、数据处理。主持和承担国家自然科学基金、河北省自然科学基金、河北省教育厅科学计划研究项目6项。以第一作者身份发表学术论文20余篇;马艳玲，讲师，主要研究方向为机器学习;董建彬，讲师，主要研究方向为图像处理与分析
通讯作者:郑一博.E-mail:yibo_zheng@126.com
更新日期/Last Update:
1900-01-01