基于改进布谷鸟搜索算法的二维Tsallis熵多阈值快速图像分割
|
摘要
为了改善二维Tsallis熵在分割复杂图像时存在计算量大、耗时长、实用性差的问题,提出基于改进布谷鸟搜索算法的二维Tsallis熵多阈值图像分割方法.首先,分析了二维Tsallis熵单阈值分割原理并将其推广到多阈值分割,同时推导出了二维Tsallis熵多阈值选取公式.其次,借鉴逐维更新评价策略,同时加入逐维扰动策略来对布谷鸟搜索算法进行改进,并用于求解二维Tsallis熵函数的最优问题.最后,用穷举法、粒子群算法、布谷鸟算法以及改进的布谷鸟算法分别对典型图像进行多阈值分割实验并将分割效果、分割数据和各算法的收敛性能分别进行比较.实验结果表明,所提算法能够快速、准确地对复杂图像进行分割.
Concerning the computationally intensive,long computing time,poor practicability and other issues of complex image segmentation,a image segmentation method with multilevel threshold of two-dimensional Tsallis entropy was proposed based on the improved cuckoo search algorithm. Firstly,the principle of two-dimensional Tsallis entropy was analyzed and the single threshold segmentation was extended to multilevel threshold segmentation. Secondly,by victoria updated assessment and disturbance strategies were used to improve the cuckoo search algorithm,and was used to solve the optimal problem of two-dimensional Tsallis entropy function. Finally,typical image segmentation experiments by using the exhaustive threshold segmentation method,particle swarm optimization algorithm,cuckoo search algorithm and improved cuckoo search algorithm. The effects and data of image Segmentation and the convergence of the algorithm were analyzed and compared respectively. Experimental results showthat the improved algorithm can quickly and efficiently resolve complex image segmentation problems.
Concerning the computationally intensive,long computing time,poor practicability and other issues of complex image segmentation,a image segmentation method with multilevel threshold of two-dimensional Tsallis entropy was proposed based on the improved cuckoo search algorithm. Firstly,the principle of two-dimensional Tsallis entropy was analyzed and the single threshold segmentation was extended to multilevel threshold segmentation. Secondly,by victoria updated assessment and disturbance strategies were used to improve the cuckoo search algorithm,and was used to solve the optimal problem of two-dimensional Tsallis entropy function. Finally,typical image segmentation experiments by using the exhaustive threshold segmentation method,particle swarm optimization algorithm,cuckoo search algorithm and improved cuckoo search algorithm. The effects and data of image Segmentation and the convergence of the algorithm were analyzed and compared respectively. Experimental results showthat the improved algorithm can quickly and efficiently resolve complex image segmentation problems.
引文
[1]Long Jian-wu.Research on Key techniques of image thresholding[D].Jilin:Jinlin University,2014.
[2]Cao Jian-nong.Review on image segmentation based on entropy[J].Pattern Recognition and Artificial Intelligence,2012,25(6):958-971.
[3]Zhang Xin-ming,Li Shuang-qun,Zheng Yan-bin.Preserving-Moment Principle-based 2-D shannon entropy image thresholding method and its fast recursive implementation[J].Computer Science,2012,39(1):276-280.
[4]Lin Qian-qian,Ou Cong-jie.Application of two dimensional Tsallis entropy in image thresholding segmentation[J].Transducer and M icrosystem Technologies,2014,33(7):150-153.
[5]Pedram G,Micael S C,Jon A B,et al.An efficient method for segmentation of images based on fractional calculus and natural selection[J].Expert Systems w ith Applications,2012,39:12407-12417.
[6]Lan Jin-hui,Zeng Yi-liang.Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram[J].Optik-Int.J.Light Electron Opt,2013,124(18):3756-3760.
[7]Cui Li-qun,Song Xiao,Li Hong-xu,et al.Multilevel thresholding image segmentation based on improved artificial fish sw arm algorithm[J].Computer Science,2014,41(8):306-310.
[8]Chen Kai,Chen Fang,Dai Min,et al.Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm[J].Optics and Precision Engineering,2014,22(2):517-523.
[9]Valian E,Mohanna S,Tavakoli S.Improved cuckoo search algorithm for global optimization[J].Int.J.Communications and Information Technology,2011,1(1):31-44.
[10]Tang Xu-dong,Pang Yong-jie,Zhang Tie-dong,et al.Detection of objects in underw ater images based on the tw o-dimensional tsallis entropy[J].Robot,2010,32(5):291-297.
[11]Wu Yi-quan,Song Yu,Zhou Huai-chun.State identification of boiler combustion flame images based on gray entropy multiple thresholding and support vector machine[J].Proceedings of the CSEE,2013,33(20):66-73.
[12]Wu Yi-quan,Ji Yang,Shen Yi,et al.Marine spill oil SAR image segmentation based on Tsallis entropy and improved Chan Vese model[J].Journal of Remote Sensing,2012,16(4):678-690.
[13]Wang Li-jin,Yin Yi-long,Zhong Yi-wen.Cuckoo search algorithm w ith dimension by dimension improvement[J].Ruan Jian Xue Bao/Journal of Softw are,2013,24(11):2687-2698.
[1]龙建武.图像阈值分割关键技术研究[D].吉林:吉林大学,2014.
[2]曹建农.图像分割的熵方法综述[J].模式识别与人工智能,2012,25(6):958-971.
[3]张新明,李双群,郑延斌.矩不变调整的二维Shannon熵图像阈值分割及其快速实现[J].计算机科学,2012,39(1):276-280.
[4]林倩倩,欧聪杰.二维Tsallis熵在图像阈值分割中的应用[J].传感器与微系统,2014,33(7):150-153.
[7]崔丽群,宋晓,李鸿绪,等.基于改进鱼群算法的多阈值图像分割[J].计算机科学,2014,41(8):306-310.
[8]陈恺,陈芳,戴敏,等.基于萤火虫算法的二维熵多阈值快速图像分割[J].光学精密工程,2014,22(2):517-523.
[10]唐旭东,庞永杰,张铁栋,等.基于2维Tsallis熵的水下图像目标检测[J].机器人,2010,32(5):291-297.
[11]吴一全,宋昱,周怀春.基于灰度熵多阈值分割和SVM的火焰图像状态识别[J].中国电机工程学报,2013,33(20):66-73.
[12]吴一全,吉玚,沈毅,等.Tsallis熵和改进CV模型的海面溢油SAR图像分割[J].遥感学报,2012,16(4):678-690.
[13]王李进,尹义龙,钟一文.逐维改进的布谷鸟搜索算法[J].软件学报,2013,24(11):2687-2698.
[2]Cao Jian-nong.Review on image segmentation based on entropy[J].Pattern Recognition and Artificial Intelligence,2012,25(6):958-971.
[3]Zhang Xin-ming,Li Shuang-qun,Zheng Yan-bin.Preserving-Moment Principle-based 2-D shannon entropy image thresholding method and its fast recursive implementation[J].Computer Science,2012,39(1):276-280.
[4]Lin Qian-qian,Ou Cong-jie.Application of two dimensional Tsallis entropy in image thresholding segmentation[J].Transducer and M icrosystem Technologies,2014,33(7):150-153.
[5]Pedram G,Micael S C,Jon A B,et al.An efficient method for segmentation of images based on fractional calculus and natural selection[J].Expert Systems w ith Applications,2012,39:12407-12417.
[6]Lan Jin-hui,Zeng Yi-liang.Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram[J].Optik-Int.J.Light Electron Opt,2013,124(18):3756-3760.
[7]Cui Li-qun,Song Xiao,Li Hong-xu,et al.Multilevel thresholding image segmentation based on improved artificial fish sw arm algorithm[J].Computer Science,2014,41(8):306-310.
[8]Chen Kai,Chen Fang,Dai Min,et al.Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm[J].Optics and Precision Engineering,2014,22(2):517-523.
[9]Valian E,Mohanna S,Tavakoli S.Improved cuckoo search algorithm for global optimization[J].Int.J.Communications and Information Technology,2011,1(1):31-44.
[10]Tang Xu-dong,Pang Yong-jie,Zhang Tie-dong,et al.Detection of objects in underw ater images based on the tw o-dimensional tsallis entropy[J].Robot,2010,32(5):291-297.
[11]Wu Yi-quan,Song Yu,Zhou Huai-chun.State identification of boiler combustion flame images based on gray entropy multiple thresholding and support vector machine[J].Proceedings of the CSEE,2013,33(20):66-73.
[12]Wu Yi-quan,Ji Yang,Shen Yi,et al.Marine spill oil SAR image segmentation based on Tsallis entropy and improved Chan Vese model[J].Journal of Remote Sensing,2012,16(4):678-690.
[13]Wang Li-jin,Yin Yi-long,Zhong Yi-wen.Cuckoo search algorithm w ith dimension by dimension improvement[J].Ruan Jian Xue Bao/Journal of Softw are,2013,24(11):2687-2698.
[1]龙建武.图像阈值分割关键技术研究[D].吉林:吉林大学,2014.
[2]曹建农.图像分割的熵方法综述[J].模式识别与人工智能,2012,25(6):958-971.
[3]张新明,李双群,郑延斌.矩不变调整的二维Shannon熵图像阈值分割及其快速实现[J].计算机科学,2012,39(1):276-280.
[4]林倩倩,欧聪杰.二维Tsallis熵在图像阈值分割中的应用[J].传感器与微系统,2014,33(7):150-153.
[7]崔丽群,宋晓,李鸿绪,等.基于改进鱼群算法的多阈值图像分割[J].计算机科学,2014,41(8):306-310.
[8]陈恺,陈芳,戴敏,等.基于萤火虫算法的二维熵多阈值快速图像分割[J].光学精密工程,2014,22(2):517-523.
[10]唐旭东,庞永杰,张铁栋,等.基于2维Tsallis熵的水下图像目标检测[J].机器人,2010,32(5):291-297.
[11]吴一全,宋昱,周怀春.基于灰度熵多阈值分割和SVM的火焰图像状态识别[J].中国电机工程学报,2013,33(20):66-73.
[12]吴一全,吉玚,沈毅,等.Tsallis熵和改进CV模型的海面溢油SAR图像分割[J].遥感学报,2012,16(4):678-690.
[13]王李进,尹义龙,钟一文.逐维改进的布谷鸟搜索算法[J].软件学报,2013,24(11):2687-2698.