{"title":"多目标分割的高级验证","authors":"M. Lukac, Almas Zhurtanov, Aizhan Ospanova","doi":"10.1109/DT.2016.7557169","DOIUrl":null,"url":null,"abstract":"In this paper we present a relational analysis and verification of multi-labeled images as a result fo semantic segmentation. In semantic segmentation the result is a set of labeled regions but rarely this result is subject to verification that would allow to determine induce the reliability of this segmentation. We show that using shape measure, co-occurrence statistics and specially crafted weighted function we can estimate the correctness of a semantic segmentation up to 92%.","PeriodicalId":281446,"journal":{"name":"2016 International Conference on Information and Digital Technologies (IDT)","volume":"146-147 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"High-level verification of multi-object segmentation\",\"authors\":\"M. Lukac, Almas Zhurtanov, Aizhan Ospanova\",\"doi\":\"10.1109/DT.2016.7557169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a relational analysis and verification of multi-labeled images as a result fo semantic segmentation. In semantic segmentation the result is a set of labeled regions but rarely this result is subject to verification that would allow to determine induce the reliability of this segmentation. We show that using shape measure, co-occurrence statistics and specially crafted weighted function we can estimate the correctness of a semantic segmentation up to 92%.\",\"PeriodicalId\":281446,\"journal\":{\"name\":\"2016 International Conference on Information and Digital Technologies (IDT)\",\"volume\":\"146-147 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Information and Digital Technologies (IDT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DT.2016.7557169\",\"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 International Conference on Information and Digital Technologies (IDT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DT.2016.7557169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High-level verification of multi-object segmentation
In this paper we present a relational analysis and verification of multi-labeled images as a result fo semantic segmentation. In semantic segmentation the result is a set of labeled regions but rarely this result is subject to verification that would allow to determine induce the reliability of this segmentation. We show that using shape measure, co-occurrence statistics and specially crafted weighted function we can estimate the correctness of a semantic segmentation up to 92%.
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