基于互补空间信息的多目标进化聚类图像分割
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摘要
现有的多目标进化聚类算法应用于图像分割时,没有考虑图像的任何空间信息,使得该类算法在含噪图像上的分割性能不理想。该文鉴于图像的局部空间信息和非局部空间信息的互补性,试图将这两种空间信息同时引入到聚类有效性函数中,构造了融合互补空间信息的目标函数,进而提出了应用于图像分割的基于互补空间信息的多目标进化聚类算法。该算法采用染色体可变长编码策略在进化过程中自动确定图像分割数目,减少了人为干预。自然图像的分割实验表明,该算法不但能在含噪图像上取得较为满意的分割性能,而且适用于多种类型的含噪图像。
When existing multi-objective evolutionary clustering algorithms is applied to image segmentation, it can not obtain satisfactory segmentation performance on an image corrupted by noise due to no consideration of any spatial information derived from the image. Based on the complementarity of the local spatial information and the non local spatial information of the image, these two kinds of spatial information are introduced into a cluster validity function, and a novel objective function with complementary spatial information is constructed, and then a multi-objective evolutionary clustering algorithm with complementary spatial information for image segmentation is proposed. In order to reduce human intervention, the variable string length real coded technique is adopted to determine automatically the number of clusters during the evolving process. Natural image segmentation experiments show that the proposed method not only can obtain satisfactory segmentation performance on noisy images, but also can be suitable for many types of noisy images.
When existing multi-objective evolutionary clustering algorithms is applied to image segmentation, it can not obtain satisfactory segmentation performance on an image corrupted by noise due to no consideration of any spatial information derived from the image. Based on the complementarity of the local spatial information and the non local spatial information of the image, these two kinds of spatial information are introduced into a cluster validity function, and a novel objective function with complementary spatial information is constructed, and then a multi-objective evolutionary clustering algorithm with complementary spatial information for image segmentation is proposed. In order to reduce human intervention, the variable string length real coded technique is adopted to determine automatically the number of clusters during the evolving process. Natural image segmentation experiments show that the proposed method not only can obtain satisfactory segmentation performance on noisy images, but also can be suitable for many types of noisy images.
引文
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[22]Maulik U and Bandyopadhyay S.Performance evaluation of some clustering algorithms and validity indices[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(12):1650-1654.
[23]Wu M and Schlkopf B.A local learning approach for clustering[C].Advances in Neural Information Processing Systems,Vancouver B.C.,Canada,2007:1529-1536.
[2]范朝冬,欧阳红林,张英杰.基于小概率策略的Otsu图像分割方法[J].电子与信息学报,2013,35(9):2081-2087.Fan Chao-dong,Ouyang Hong-lin,and Zhang Ying-jie.Small probability strategy based Otsu thresholding method for image segmentation[J].Journal of Electronics&Information Technology,2013,35(9):2081-2087.
[3]Gong Mao-guo,Liang Yan,Shi Jiao,et al..Fuzzy c-means clustering with local information and kernel metric for image segmentation[J].IEEE Transactions on Image Processing,2013,22(2):573-584.
[4]Caldairoua B,Passata N,Habas P A,et al..A non-local fuzzy segmentation method:application to brain MRI[J].Pattern Recognition,2011,44(9):1916-1927.
[5]Tarabalka Y,Chanussot J,and Benediktsson J A.Segmentation and classification of hyperspectral images using watershed transformation[J].Pattern Recognition,2010,43(7):2367-2379.
[6]黄志坚,黎湘,徐帆江.基于视觉复杂度的自适应尺度遥感影像分割[J].电子与信息学报,2013,35(8):1786-1792.Huang Zhi-jian,Li Xiang,and Xu Fan-jiang.An adaptive scale segmentation for remote sensing image based-on visual complexity[J].Journal of Electronics&Information Technology,2013,35(8):1786-1792.
[7]Wei B C and Mandava R.Multiobjective optimization approaches in image segmentation–the directions and challenges[J].International Journal of Advances in Soft Computing and Its Applications,2010,2(1):40-65.
[8]Ooi W S and Lim C P.Multi-objective image segmentation with an interactive evolutionary computation approach[J].Journal of Intelligent and Fuzzy Systems,2013,24(2):239-249.
[9]Bandyopadhyay S and Maulik U.An evolutionary technique based on k-means algorithm for optimal clustering in RN[J].Information Sciences,2002,146(1/2/3/4):221-237.
[10]Caballero R,Laguna M,Marti R,et al..Multiobjective clustering with metaheuristic optimization technology[R].Technical Report of Leeds School of Business in the University of Colorado at Boulder,2006.
[11]Handl J and Knowles J.An evolutionary approach to multiobjective clustering[J].IEEE Transactions on Evolutionary Computation,2006,11(1):56-76.
[12]Bandyopadhyay S,Maulik U,and Mukhopadhyay A.Multiobjective genetic clustering for pixel classification in remote sensing imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1506-1511.
[13]Mukhopadhyay A,Bandyopadhyay S,and Maulik U.Clustering using multiobjective genetic algorithm and its application to image segmentation[C].Proceedings of the International Conference on IEEE Systems,Man and Cybernetics,Taibei,2006:2678-2683.
[14]Mukhopadhyay A,Maulik U,and Bandyopadhyay S.Multiobjective genetic clustering with ensemble among Pareto front solutions:application to MRI brain image segmentation[C].Proceedings of the International Conference on Advances in Pattern Recognition,Kolkata,India,2009:236-239.
[15]Pakhira M,Bandyopadhyay S,and Maulik U.Validity index for crisp and fuzzy clusters[J].Pattern Recognition,2004,37(3):487-501.
[16]Mukhopadhyay A and Maulik U.A multiobjective approach to MR brain image segmentation[J].Applied Soft Computing,2011,11(1):872-880.
[17]Chen S C and Zhang D Q.Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J].IEEE Transactions on System,Man,and Cybernetics,Part B:Cybernetics,2004,34(4):1907-1916.
[18]Cai W L,Chen S C,and Zhang D Q.Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J].Pattern Recognition,2007,40(7):825-838.
[19]Zhao F,Jiao L C,Liu H Q,et al..A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation[J].Signal Processing,2011,91(4):988-999.
[20]Zhao F,Jiao L C,and Liu H Q.Fuzzy c-means clustering with non local spatial information for noisy image segmentation[J].Frontiers of Computer Science in China,2011,5(1):45-56.
[21]Deb K,Agrawal S,Pratab A,et al..A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-II[C].Proceedings of the Parallel Problem Solving from Nature VI Conference,Springer,Lecture Notes in Computer Science No.1917,Paris,France,2000:849-858.
[22]Maulik U and Bandyopadhyay S.Performance evaluation of some clustering algorithms and validity indices[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(12):1650-1654.
[23]Wu M and Schlkopf B.A local learning approach for clustering[C].Advances in Neural Information Processing Systems,Vancouver B.C.,Canada,2007:1529-1536.