K-means clustering algorithm is an important algorithm in unsupervised learning and plays an important role in big data processing, computer vision and other research fields. However, due to its sensitivity to initial...K-means clustering algorithm is an important algorithm in unsupervised learning and plays an important role in big data processing, computer vision and other research fields. However, due to its sensitivity to initial partition, outliers, noise and other factors, the clustering results in data analysis, image segmentation and other fields are unstable and weak in robustness. Based on the fast global K-means clustering algorithm, this paper proposed an improved K-means clustering algorithm. Through the neighborhood filtering mechanism, the points in the neighborhood of the selected initial clustering center have not participated in the selection of the next initial clustering center, which can effectively reduce the randomness of initial partition and improve the efficiency of initial partition. Mahalanobis distance was used in the clustering process to better consider the global nature of data. Compared with the traditional clustering algorithm and other optimization algorithms, the results of real data set testing are significantly improved.展开更多
由于马尔科夫随机场(Markov Random Fields,MRF)区域标识模型的滤波效应,在合成孔径雷达(SAR)图像处理过程中,细节结构会被部分保留或者完全丢失。本文提出一种基于散射描述子的自适应邻域系统隐MRF(Hidden MRF,HMRF)图像分割方法,以实...由于马尔科夫随机场(Markov Random Fields,MRF)区域标识模型的滤波效应,在合成孔径雷达(SAR)图像处理过程中,细节结构会被部分保留或者完全丢失。本文提出一种基于散射描述子的自适应邻域系统隐MRF(Hidden MRF,HMRF)图像分割方法,以实现更好地保留图像细节特征和边缘区域,从而改善图像的分割效果。为了提高可靠性和自适应性,将模糊c均值(Fuzzy c-means,FCM)聚类算法与散射变换相结合,实现邻域形状的自适应选择。从不同的邻域形状中,选择具有最高模糊隶属度的邻域形状进行HMRF区域标识过程。实验结果表明,相比较于一般HMRF使用固定形状的邻域系统,本文所提出的算法改善了分割效果,特别是图像细节结构信息得到了很好的保护。展开更多
文摘K-means clustering algorithm is an important algorithm in unsupervised learning and plays an important role in big data processing, computer vision and other research fields. However, due to its sensitivity to initial partition, outliers, noise and other factors, the clustering results in data analysis, image segmentation and other fields are unstable and weak in robustness. Based on the fast global K-means clustering algorithm, this paper proposed an improved K-means clustering algorithm. Through the neighborhood filtering mechanism, the points in the neighborhood of the selected initial clustering center have not participated in the selection of the next initial clustering center, which can effectively reduce the randomness of initial partition and improve the efficiency of initial partition. Mahalanobis distance was used in the clustering process to better consider the global nature of data. Compared with the traditional clustering algorithm and other optimization algorithms, the results of real data set testing are significantly improved.
文摘由于马尔科夫随机场(Markov Random Fields,MRF)区域标识模型的滤波效应,在合成孔径雷达(SAR)图像处理过程中,细节结构会被部分保留或者完全丢失。本文提出一种基于散射描述子的自适应邻域系统隐MRF(Hidden MRF,HMRF)图像分割方法,以实现更好地保留图像细节特征和边缘区域,从而改善图像的分割效果。为了提高可靠性和自适应性,将模糊c均值(Fuzzy c-means,FCM)聚类算法与散射变换相结合,实现邻域形状的自适应选择。从不同的邻域形状中,选择具有最高模糊隶属度的邻域形状进行HMRF区域标识过程。实验结果表明,相比较于一般HMRF使用固定形状的邻域系统,本文所提出的算法改善了分割效果,特别是图像细节结构信息得到了很好的保护。