{"title":"IDA -迭代数据分析应用于颜色矢量量化","authors":"T. D’orazio, C. Guaragnella","doi":"10.1109/ISCCSP.2004.1296230","DOIUrl":null,"url":null,"abstract":"An automatic iterative unsupervised data analysis tool is presented as a modification of well known Isodata algorithm. The main feature is its complete blindness and repeatability of the obtained results. It automatically selects a suitable number of features able to describe the whole data set requiring only one input parameter. As an application, color vector quantization has been addressed, both on real and on synthetic data sets, showing good performances.","PeriodicalId":146713,"journal":{"name":"First International Symposium on Control, Communications and Signal Processing, 2004.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IDA - iterative data analysis applied to color vector quantization\",\"authors\":\"T. D’orazio, C. Guaragnella\",\"doi\":\"10.1109/ISCCSP.2004.1296230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An automatic iterative unsupervised data analysis tool is presented as a modification of well known Isodata algorithm. The main feature is its complete blindness and repeatability of the obtained results. It automatically selects a suitable number of features able to describe the whole data set requiring only one input parameter. As an application, color vector quantization has been addressed, both on real and on synthetic data sets, showing good performances.\",\"PeriodicalId\":146713,\"journal\":{\"name\":\"First International Symposium on Control, Communications and Signal Processing, 2004.\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First International Symposium on Control, Communications and Signal Processing, 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCCSP.2004.1296230\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First International Symposium on Control, Communications and Signal Processing, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCCSP.2004.1296230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
IDA - iterative data analysis applied to color vector quantization
An automatic iterative unsupervised data analysis tool is presented as a modification of well known Isodata algorithm. The main feature is its complete blindness and repeatability of the obtained results. It automatically selects a suitable number of features able to describe the whole data set requiring only one input parameter. As an application, color vector quantization has been addressed, both on real and on synthetic data sets, showing good performances.
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