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此软件的功能是对遥感图像进行非监督分类。实现算法是k均值-This software is a remote sensing image for non-supervised classification. K-means algorithm implementation are
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c#下实现图像颜色k均值聚类
将像素点颜色分类并合并-c# implementation of color image k-means clustering to classification of color pixels and merge
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基于OpenCV的二元吗，实现了划分随机分布点集的K-均值算法。按照类别分组的输入样本顺序输出包含样本类别索引的数组labels(i)，存储在样本矩阵的第i行中。-Based on the binary OpenCV吗, implementation of the demarcation point set of randomly distributed K-means algorithm. Input in accordance with the type of packet contains
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基于K-Means的纹理图像分割，利用k-means方法实现对于纹理的图像分割-K-Means-based texture image segmentation using the k-means method implementation for texture image segmentation
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矢量量化算法的C语言实现：LBG(K-均值)聚类算法，在欧几里的距离基础上用LBG算法将输入向量聚类。-Vector quantization of C language implementation algorithm:LBG (k-means) clustering algorithm, in several European based on the distance with LBG algorithm will input vector clustering.
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Nister（2006）等人的层次聚类算法，按照多线程k-means方法进行多线程层次聚类，程序中代码对SIFT特征进行聚类。开发环境：vs2010-Implementation of hierarchical k-means, proposed by Nister et al, 2006. This is a multi-thread implementation of k-means and multi-thread implementation of the hierarchical me
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图像分割中K-means聚类算法的matlab实现-Image segmentation in the K-means clustering algorithm matlab implementation
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自己实现的K均值聚类程序，有比较详细的注释-My implementation of K-means clustering procedure, with detailed notes
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自己实现的，模糊C均值聚类的代码，在K-mean上添加了隶属度矩阵。注释详细，方便理解算法步骤-my implementation, Fuzzy C-Means clustering code in the K-mean adding a membership matrix. Notes detailed, easy to understand algorithm steps
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SIFT等局部特征的词袋模型实现。包括K-means聚类，直方图特征的形成，以及KNN分类。-SIFT local features such as word bag model implementation. Including K-means clustering to form histogram features, and KNN classification.
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图像K均值聚类，使用DIB类实现，整体比较简单-Image K-means clustering, using DIB class implementation, the overall relatively simple
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python实现用SIFT+K-MEANS+SVM图片分类(Python implementation with SIFT + K-MEANS + SVM picture classification)
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