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IMAGE CLASSIFICATION USING PARTICLE SWARM OPTIMIZATION

机译:使用粒子群优化的图像分类

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In this chapter, a new unsupervised image clustering approach which is based on the particle swarm optimization (PSO) algorithm is presented. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar pixels. The new image clustering algorithm has been applied successfully to three types of images to illustrate its wide applicability. These images include synthetic, MRI and Satellite images. A comparison between the new approach and the well-known K-means clustering algorithm is provided to show the efficiency of PSO in the area of image clustering.
机译:在本章中,提出了一种基于粒子群优化(PSO)算法的新的无监督图像聚类方法。 该算法查找用户指定数量的群集数的质心,其中每个群集组在一起相似的像素。 新的图像聚类算法已成功应用于三种类型的图像以说明其广泛的适用性。 这些图像包括合成,MRI和卫星图像。 新方法与众所周知的K-Means聚类算法之间的比较是为了显示在图像聚类区域中PSO的效率。

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