{"title":"基于K-means和粒子群算法的卫星图像聚类与优化","authors":"G. Kumar, P. P. Sarth, P. Ranjan, Sushant Kumar","doi":"10.1109/ICPEICES.2016.7853627","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":305942,"journal":{"name":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Satellite image clustering and optimization using K-means and PSO\",\"authors\":\"G. Kumar, P. P. Sarth, P. Ranjan, Sushant Kumar\",\"doi\":\"10.1109/ICPEICES.2016.7853627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":305942,\"journal\":{\"name\":\"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPEICES.2016.7853627\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEICES.2016.7853627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.