[1]刘恋,常冬霞,邓勇.动态小生境人工鱼群算法的图像分割[J].智能系统学报编辑部,2015,10(5):669-674.[doi:10.11992/tis.201501001]
 LIU lian,CHANG Dongxia,DENG Yong.An image segmentation method based on dynamic niche artificial fish-swarm algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(5):669-674.[doi:10.11992/tis.201501001]
点击复制

动态小生境人工鱼群算法的图像分割(/HTML)
分享到:

《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年5期
页码:
669-674
栏目:
出版日期:
2015-10-25

文章信息/Info

Title:
An image segmentation method based on dynamic niche artificial fish-swarm algorithm
作者:
刘恋123 常冬霞123 邓勇4
1. 北京交通大学 信息科学研究所, 北京 100044;
2. 北京交通大学 计算机与信息技术学院, 北京 100044;
3. 北京交通大学 北京现代信息科学与网络技术北京市重点实验室, 北京 100044;
4. 中国科学院 软件研究所, 北京 100190
Author(s):
LIU lian123 CHANG Dongxia123 DENG Yong4
1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;
2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
3. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China;
4. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
关键词:
人工鱼群算法图像分割聚类动态小生境进化计算
Keywords:
artificial fish-swarm algorithmimage segmentationclustering algorithmdynamic nicheevolutionary computation
分类号:
TN391.41;TP391.41
DOI:
10.11992/tis.201501001
文献标志码:
A
摘要:
为了克服传统基于聚类的图像分割算法需要指定聚类数目以及依赖初始值等缺点,提出了一种基于动态小生境的人工鱼群算法的图象分割方法。该算法将图像分割问题转化为根据图像像素特征对像素的自动聚类问题。采用更为简单的个体描述方式,每条人工鱼表示一个分割区域的一个可行解,并对进化过程中的人工鱼进行动态的划分小生境,每个小生境对应了图像分割问题中一个分割区域。通过对鱼群行为的模拟及种群的动态划分实现了对图像分割问题的分割区域中心和区域数的同时进化,实现了一种新的聚类算法,并实现了对图像的自动分割。实验结果表明:该算法可以自动地估计分割的区域数,并获得较好的分割性能。
Abstract:
In order to overcome the defects in the traditional clustering-based image segmentation algorithm, e.g., it needs to specify the number of clusters, it is sensitive to initial value, and so on, an image segmentation method based on dynamic niche artificial fish-swarm algorithm (DNAF) is presented in this paper. In the new algorithm, the image segmentation problem is transformed into an automatic pixel clustering process based on the pixel features of the image. A simpler representation is adopted, each artificial fish represents a single feasible solution of one segmented area. Moreover, the dynamic identification of the fish niches is performed at each generation to automatically evolve the optimal number of regions. Each fish niche corresponds to one segmentation region in the image segmentation problem. Therefore, the proposed DNAF algorithm implements simultaneous evolution in the center of the segmentation region and the optimal number of regions through simulation on the behaviors of fish swarm and the dynamic division of population. It thereby achieves a new clustering algorithm and automatic segmentation of an image. Experiment results demonstrate that the DNAF algorithm is able to automatically estimate the number of the segmented regions, and an excellent segmentation performance can be attained.

参考文献/References:

[1] COLEMAN G B, ANDREWS H C. Image segmentation by clustering[J]. Proceedings of the IEEE, 1979, 67(5):773-785.
[2] 阳春华, 杨尽英, 牟学民, 等. 基于聚类预分割和高低精度距离重构的彩色浮选泡沫图像分割[J]. 电子与信息学报, 2008, 30(6):1286-1290. YANG Chunhua, YANG Jinying, MOU Xuemin, et al. A segmentation method based on clustering pre-segmentation and high-low scale distance reconstruction for colour froth image[J]. Journal of Electronics & Information Technology, 2008, 30(6):1286-1290.
[3] ZHANG Jingdong, JIANG Wuhan, WANG Ruichun, et al. Brain MR image segmentation with spatial constrained K-mean algorithm and dual-tree complex wavelet transform[J]. Journal of Medical Systems, 2014, 38(9):93.
[4] 李晓磊. 一种新型的智能优化方法——人工鱼群算法[D]. 杭州:浙江大学, 2003:10-15.LI Xiaolei. A new intelligent optimization method-artificial fish school algorithm[D]. Hangzhou, China:Zhejiang University, 2003:10-15.
[5] HUANG Zhenhuang, CHEN Yidong. An improved artificial fish swarm algorithm based on hybrid behavior selection[J]. International Journal of Control and Automation, 2013, 6(5):103-116.
[6] EI-SAID S A. Image quantization using improved artificial fish swarm algorithm[J]. Soft Computing, 2014, 24(8):221-232.
[7] LIU Qing, ODAKA T, KUROIWA J, et al. Application of an artificial fish swarm algorithm in symbolic regression[J]. IEICE Transactions on Information and Systems, 2013, 96-D(4):872-885.
[8] 潘喆, 吴一全. 二维Otsu图像分割的人工鱼群算法[J]. 光学学报, 2009, 29(8):2115-2121. PAN Zhe, WU Yiquan. The two-dimensional Otsu thresholding based on fish-swarm algorithm[J]. Acta Optica Sinica, 2009, 29(8):2115-2121.
[9] 范玉军, 王冬冬, 孙明明. 改进的人工鱼群算法[J]. 重庆师范大学学报:自然科学版, 2007, 24(3):23-26. FAN Yujun, WANG Dongdong, SUN Mingming. Improved artificial fish-school algorithm[J]. Journal of Chongqing Normal University:Natural Science Edition, 2007, 24(3):23-26.
[10] 楚晓丽. K-means聚类算法和人工鱼群算法应用于图像分割技术[J]. 计算机系统应用, 2013, 22(4):92-94. CHU Xiaoli. K-means clustering algorithm and artificial fish swarm algorithm applied in image segmentation technology[J]. Computer Systems and Applications, 2013, 22(4):92-96.
[11] YANG M S, WU K L. A similarity-based robust clustering method[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2004, 26(4):434-448.
[12] LI Jianping, MARTON E B, PARKS GY T, et al. A species conserving genetic algorithm for multimodal function optimization[J]. Evolutionary Computation, 2002, 10(3):207-234.
[13] BEZDEK J C, CHRISTIAN J. Fuzzy mathematics in pattern classification[D]. New York City:Cornell University, 1973, 142-147.
[14] BORSOTTI M, CAMPADELLI P, SCHETTINI R. Quantitative evaluation of color image segmentation results[J]. Pattern Recognition Letters, 1998, 19(8):741-747.

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].智能系统学报编辑部,2007,2(01):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(5):53.
[2]陈小波,程显毅.一种基于MAS的自适应图像分割方法[J].智能系统学报编辑部,2007,2(04):80.
 CHEN Xiao-bo,CHENG Xian-yi.An adaptive image segmentation technique based on multiAgent system[J].CAAI Transactions on Intelligent Systems,2007,2(5):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].智能系统学报编辑部,2010,5(01):67.
 LIU Yong-mei,DAI Li-jie.An improved method of Kmeans image segmentation based on spatial position information[J].CAAI Transactions on Intelligent Systems,2010,5(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].智能系统学报编辑部,2010,5(06):522.
 WU Yi-quan,JI Shou-xin.Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2010,5(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].智能系统学报编辑部,2011,6(04):303.
 SHANG Qian,RUAN Qiuqi,LI Xiaoli.Target recognition and location based on binocular stereo vision[J].CAAI Transactions on Intelligent Systems,2011,6(5):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].智能系统学报编辑部,2012,7(01):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(5):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].智能系统学报编辑部,2012,7(03):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(5):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].智能系统学报编辑部,2015,10(01):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(5):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].智能系统学报编辑部,2015,10(01):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(5):103.[doi:10.3969/j.issn.1673-4785.201403040]
[10]龙鹏,鲁华祥.方差不对称先验信息引导的全局阈值分割方法[J].智能系统学报编辑部,2015,10(5):663.[doi:10.11992/tis.201412022]
 LONG Peng,LU Huaxiang.Global threshold segmentation technique guided by prior knowledge with asymmetric variance[J].CAAI Transactions on Intelligent Systems,2015,10(5):663.[doi:10.11992/tis.201412022]

备注/Memo

备注/Memo:
收稿日期:2015-01-01;改回日期:。
基金项目:国家自然科学基金资助项目(61100141);中央高校基本科研业务费资助项目(2013JBM021);中央高校基本科研业务费专项基金资助项目(2012RC044).
作者简介:刘恋,男,1990年生,硕士研究生,主要研究方向为智能优化算法和图像分割;常冬霞,女,1977年生,博士,副教授,主要研究方向为进化计算、非监督分类算法、图像分割以及图像分类等。主持国家自然科学基金项目1项,中央高校基本科研业务费1项。发表学术论文10余篇,其中SCI收录4篇,EI收录2篇;邓勇,男,1974年生,博士,副研究员,主要研究方向为智能信息处理、数据库系统技术及应用等。主持和参与国家863项目1项,北京市自然科学基金项目1项。发表学术论文20余篇,其中EI收录10余篇。
通讯作者:邓勇.E-mail:dengyong@iscas.ac.cn.
更新日期/Last Update: 2015-11-16