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In this paper, an unsupervised SAR image segmentation algorithm (QEAGMM) based on quantum-inspired evolutionary Gaussian Mixture Models (GMM) is proposed. The method first divides the original image into small blocks. Then, the heterogeneous and homogeneous blocks are obtained using FCM clustering. Finally, the parameters of gaussian mixture model are trained by expectation-maximization (EM) algorithm...
An effective clustering algorithm based on binary tree is proposed in this paper. A roughly extraction of the color image is gotten by constructing the self-adapting binary tree. A C_means clustering algorithm is designed based on the extraction. This algorithm is to improve the segmentation's accuracy of the binary tree's leaves. Experiments have approved that this new method can be implemented efficiently...
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