利用特征场进化的图像分割方法

计算机科学 ›› 2014, Vol. 41 ›› Issue (Z6): 167-173.

利用特征场进化的图像分割方法

吴涛

湛江师范学院信息科学与技术学院湛江524048

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受广东省自然科学基金(S2013040014926),广东高校优秀青年创新人才培养计划项目(2012LYM_0092),湛江师范学院科学研究项目博士专项(ZL1301),国家973重点基础研究发展计划项目(2012CB719903)资助

Feature Field-based Evolution Method for Image Segmentation

WU Tao

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 针对图像分割中最优阈值的选择问题,在数据场机制的基础上提出了一种高维图像分割方法。将局部灰度特征与Tamura纹理度量相结合,如粗糙度、对比度、方向度等,尽可能提取足够的图像信息；将每个包含多特征的像素视作具有一定质量的质点,在图像特征空间上建立特征场；假设最优阈值为潜在的进化方向,通过因动态数据场中相互作用所导致的质点之间自适应吸引和排斥实现特征场的协同进化,并利用少数服从多数的投票法则确定最终分割结果。实验结果表明,所提出的方法在不明显增加时间耗费的情况下,取得了较好的分割效果,具有合理性和有效性。

关键词: 图像分割,数据场,多维阈值,进化算法,纹理中图法分类号TP391.41文献标识码A

Abstract: In order to correctly select the optimal threshold for image segmentation,a novel method of high-dimensional thresholding based on data field mechanism was proposed．Combing the local grayscale and Tamura’s texture model,including coarseness,contrast and directionality,enough image information is extracted as far as possible．Taking each pixel with multi-features as a data particle with certain mass,a feature field is generated in the image feature space．Assuming the optimal threshold is latent optimal evolution direction,and feature field-based coevolution is achieved by adaptive attraction and repulsion among particles because of the interactions in dynamic data field,and the final result is determined by majority rule．The experimental results suggest that the new method obtains better performance without obviously increasing the time cost,and it is efficient and effective.

Key words: Image segmentation,Data field,Multi-dimensional thresholding,Evolution algorithm,Texture

吴涛. 利用特征场进化的图像分割方法[J]. 计算机科学, 2014, 41(Z6): 167-173. https://doi.org/

WU Tao. Feature Field-based Evolution Method for Image Segmentation[J]. Computer Science, 2014, 41(Z6): 167-173. https://doi.org/

参考文献

[1] 邹小林,冯国灿．基于视觉感知和等周割的二维阈值分割算法 [J].计算机科学,2013,40(6):303-307
[2] 刘哲,宋余庆,宋收珊．多元Chebyshev正交多项式混合模型及其在医学图像分割中的应用 [J]．计算机科学,2013,40(2):279-283
[3] 王娴,周宇,云挺,等．基于Curvelet变换的荻草细胞图像分割 [J].计算机科学,2012,39(11):277-279
[4] 刘健庄,粟文青．灰度图像的二维Otsu自动阈值分割法 [J]．自动化学报,1993,19(1):101-105
[5] Gong Jian,Li Li-yuan,Chen Wei-nan．Fast recursive algorithm for two-dimensional thresholding [J].Pattern Recognition,1998,31(3):295-300
[6] 吴一全,张金矿．基于改进的二维最大熵及粒子群递推的图像分割 [J]．计算机辅助设计与图形学学报,2008,20(10):1338-1344
[7] 聂方彦,高潮,郭永彩．灰度图像二维最小类方差递推及差分演化的快速分割 [J]．计算机辅助设计与图形学学报,2010,22(11):1867-1873
[8] 吴涛,秦昆,区磊海,等．图像二维阈值分割的数据场方法 [J]．华南理工大学学报:自然科学版,2010,38(11):128-134
[9] 李娜,李元香．基于自适应粒子群算法和数据场的图像二维阈值分割 [J]．计算机辅助设计与图形学学报,2012,24(5):628-635
[10] 景晓军,李剑峰,刘郁林．一种基于三维最大类间方差的图像分割算法 [J]．电子学报,2003,31(9):1281-1285
[11] 范九伦,赵凤,张雪峰．三维Otsu阈值分割方法的递推算法 [J]．电子学报,2007,35(7):1398-1403
[12] Wang Na,Li Xia,Chen Xiao-hong．Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm[J]．Pattern Recognition Letters,2010,31(13):1809-1815
[13] 林正春,王知衍,张艳青．最优进化图像阈值分割算法 [J]．计算机辅助设计与图形学学报,2010,22(7):1201-1206
[14] Lin Zhen-chun,Wang Zhi-yan,Zhang Yan-qing．Image thresholding using particle swarm optimization [C]∥Proceedings of International Conference on MultiMedia and Information Techno-logy．IEEE,Three Gorges,USA,2008:245-248
[15] Zhao Xin,Lee M-E,Kim S-H．Improved image thresholding using ant colony optimization algorithm [C]∥Proceedings of the 2008International Conference on Advanced Language Proces-sing and Web Information Technology．IEEE Computer Society,Dalian,China,2008:210-215
[16] Wu Tao,Qin Kun．Data field-based mechanism for three-dimensional thresholding [J]．Neurocomputing,2012,97c:278-296
[17] Ilea D E,Whelan P F．Image segmentation based on the integration of color-texture descriptors-A review [J]．Pattern Recognition,2011,44(10/11):2479-2501
[18] 刘宁宁,田捷,胡志刚,等．基于神经网络的纹理和灰度信息融合方法[J]．软件学报,1999,10(6):575-580
[19] 袁宝峰,吴乐华,曾伟．基于纹理与灰度协同进化的图像分割算法 [J]．计算机应用,2009,29(1):54-58
[20] Hideyuki T,Shunji M,Takashi Y．Textural Features Corre-sponding to Visual Perception [J]．IEEE Transactions on Systems,Man and Cybernetics,1978,8(6):460-473
[21] 赵海英,徐光美,彭宏．纹理粗糙度度量算法的性能比较 [J]．计算机科学,2011,38(6):288-293
[22] Rosin P L．Unimodal thresholding [J]．Pattern Recognition,2001,34(11):2083-2096
[23] Li De-yi,Du Yi．Artificial intelligent with uncertainty [M]．Boca Raton:Chapman & Hall/CRC,2007
[24] Hoang M A,Geusebroek J-M,Smeulders A W M．Color texture measurement and segmentation [J]．Signal Processing,2005,85(2):265-275
[25] Shi Jian-bo,Malik J．Normalized cuts and image segmentation[J]．IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-906
[26] Zhang Kai-hua,Zhang Lei,Song Hui-hui,et al．Active contours with selective local or global segmentation:A new formulation and level set method [J]．Image and Vision Computing,2010,28(4):668-676

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed