{"title":"Homogeneity Testing for Unlabeled Data: A Performance Evaluation","authors":"Wu Z.Y.","doi":"10.1006/cgip.1993.1028","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we address the problem of testing homogeneity for unlabeled pixels observed in a subimage. Homogeneity testing is an essential component in split-and-merge segmentation algorithm. Two types of homogeneity tests are involved: tests for labeled data when deciding on merges between regions and tests for unlabeled data when deciding whether to split a region. In our study, we focus on images that are modeled as a mosaic of uniform regions corrupted by additive Gaussian noise. Using this model, we present a statistical analysis on the performance of two commonly used approaches for testing homogeneity of unlabeled data based on the region/subregion similarity and the data dispersion, respectively. We also propose and evaluate a new hierarchical homogeneity testing scheme for unlabeled data. The most important finding of this study is that the tests based on region/subregion similarity have a low power on average of detecting inhomogeneity in unlabeled data.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 5","pages":"Pages 370-380"},"PeriodicalIF":0.0000,"publicationDate":"1993-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1028","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CVGIP: Graphical Models and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S104996528371028X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
In this paper, we address the problem of testing homogeneity for unlabeled pixels observed in a subimage. Homogeneity testing is an essential component in split-and-merge segmentation algorithm. Two types of homogeneity tests are involved: tests for labeled data when deciding on merges between regions and tests for unlabeled data when deciding whether to split a region. In our study, we focus on images that are modeled as a mosaic of uniform regions corrupted by additive Gaussian noise. Using this model, we present a statistical analysis on the performance of two commonly used approaches for testing homogeneity of unlabeled data based on the region/subregion similarity and the data dispersion, respectively. We also propose and evaluate a new hierarchical homogeneity testing scheme for unlabeled data. The most important finding of this study is that the tests based on region/subregion similarity have a low power on average of detecting inhomogeneity in unlabeled data.