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基于SGNN的彩色图像分类
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作者 刘纯平 孙忱 《江南大学学报(自然科学版)》 CAS 2007年第6期838-841,共4页
将神经网络用于遥感图像分类处理取得了一些成果,但已有的方法存在计算量大、需要用户设置网络结构和较多参数等缺点.SGNN(Self-Generating Neural Network)是一类自组织神经网络,它不需要用户指定网络结构和学习参数,而且不需要迭代学... 将神经网络用于遥感图像分类处理取得了一些成果,但已有的方法存在计算量大、需要用户设置网络结构和较多参数等缺点.SGNN(Self-Generating Neural Network)是一类自组织神经网络,它不需要用户指定网络结构和学习参数,而且不需要迭代学习,是一类特点突出的神经网络.基于SGNN进行彩色图像分类,实验结果表明,该方法用于彩色图像的分类是可行的. 展开更多
关键词 彩色图像分类 自生成神经网络 非监督学习
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Color image segmentation using mean shift and improved ant clustering 被引量:3
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作者 刘玲星 谭冠政 M.Sami Soliman 《Journal of Central South University》 SCIE EI CAS 2012年第4期1040-1048,共9页
To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can ... To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability. 展开更多
关键词 color image segmentation improved ant clustering graph partition mean shift
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