基于K-means和粒子群算法的卫星图像聚类与优化

G. Kumar, P. P. Sarth, P. Ranjan, Sushant Kumar
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引用次数: 5

摘要

粒子群优化(PSO)是一种基于种群的优化技术,受动物和鸟类的社会行为的启发,可以有效地解决大规模的非线性优化问题。本文简要概述了近四十年来聚类技术的基本概念,并对不同的相似性度量方法进行了简要回顾。采用K-means对印度孟买城市卫星图像与山魈、小丑等标准图像在HSV色彩空间进行聚类。利用粒子群算法优化聚类结果,并测量点到质心距离的聚类内和。结果表明,我们的方法可以产生比单独使用K均值更紧凑和优化的聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Satellite image clustering and optimization using K-means and PSO
Particle swarm optimization (PSO) is a population based optimization technique, inspired by social behavior of animal and birds, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a brief overview of the basic concepts of clustering techniques proposed in last four decades and a quick review of different similarity measure has been done. K-means is implemented to cluster satellite image of city Mumbai (India) and standard image such as mandrill and clown in HSV color space. PSO is used to optimize clusters results from k-means and within-cluster sums of point-to-centroid distances are measured. The results illustrate that our approach can produce more compact and optimized clusters than the K means alone.
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