Sree Ramya S. P. Malladi, Sundaresh Ram, Jeffrey J. Rodríguez
{"title":"A Ground-Truth Fusion Method for Image Segmentation Evaluation","authors":"Sree Ramya S. P. Malladi, Sundaresh Ram, Jeffrey J. Rodríguez","doi":"10.1109/SSIAI.2018.8470317","DOIUrl":null,"url":null,"abstract":"Image segmentation evaluation is popularly categorized into two different approaches based on whether the evaluation uses a human expert’s manual segmentation as a reference or not. When comparing automated segmentation against manual segmentation, also referred to as the ground-truth segmentation, multiple ground-truths are usually available. Much research has been done on analysis of segmentation algorithms and performance metrics, but very little study has been done on analyzing techniques for ground-truth fusion from multiple ground-truth segmentations. We propose a hybrid ground-truth fusion technique for image segmentation evaluation and compare it with other existing ground-truth fusion methods on a data set having multiple ground-truths at various coarseness levels. Qualitative and quantitative results show that the proposed method provides improved segmentation evaluation performance.","PeriodicalId":422209,"journal":{"name":"2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSIAI.2018.8470317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Image segmentation evaluation is popularly categorized into two different approaches based on whether the evaluation uses a human expert’s manual segmentation as a reference or not. When comparing automated segmentation against manual segmentation, also referred to as the ground-truth segmentation, multiple ground-truths are usually available. Much research has been done on analysis of segmentation algorithms and performance metrics, but very little study has been done on analyzing techniques for ground-truth fusion from multiple ground-truth segmentations. We propose a hybrid ground-truth fusion technique for image segmentation evaluation and compare it with other existing ground-truth fusion methods on a data set having multiple ground-truths at various coarseness levels. Qualitative and quantitative results show that the proposed method provides improved segmentation evaluation performance.