基于遗传算法的图像分割定量测试

H. Al-Muhairi, M. Fleury, A. Clark
{"title":"基于遗传算法的图像分割定量测试","authors":"H. Al-Muhairi, M. Fleury, A. Clark","doi":"10.1109/SITIS.2007.100","DOIUrl":null,"url":null,"abstract":"Quantitative testing of segmentation algorithms implies rigorous testing against ground truth segmentations. Though under-reported in the literature, the performance of a segmentation algorithm depends on the choice of input parameters. The paper reports wide variety both in evaluation time and segmentation results for an example mean-shift algorithm. When testing extends over an algorithmpsilas parameter space, then the search for satisfactory settings has a considerable cost in time. This paper considers the use of a genetic algorithm (GA) to avoid an exhaustive search. As application of the GA drastically reduces search times, the paper investigates how best to apply the GA in terms of initial candidate population, convergence speed, and application of a final polishing round. The GA parameter search forms part of a three-component computation environment aimed at automating the search and reducing the evaluation time. The first component relies on scripted testing and collation of results. The second component transfers to a commodity cluster computer. And the third component applies a genetic algorithm to avoid an exhaustive search.","PeriodicalId":234433,"journal":{"name":"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Computationally Efficient Quantitative Testing of Image Segmentation with a Genetic Algorithm\",\"authors\":\"H. Al-Muhairi, M. Fleury, A. Clark\",\"doi\":\"10.1109/SITIS.2007.100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantitative testing of segmentation algorithms implies rigorous testing against ground truth segmentations. Though under-reported in the literature, the performance of a segmentation algorithm depends on the choice of input parameters. The paper reports wide variety both in evaluation time and segmentation results for an example mean-shift algorithm. When testing extends over an algorithmpsilas parameter space, then the search for satisfactory settings has a considerable cost in time. This paper considers the use of a genetic algorithm (GA) to avoid an exhaustive search. As application of the GA drastically reduces search times, the paper investigates how best to apply the GA in terms of initial candidate population, convergence speed, and application of a final polishing round. The GA parameter search forms part of a three-component computation environment aimed at automating the search and reducing the evaluation time. The first component relies on scripted testing and collation of results. The second component transfers to a commodity cluster computer. And the third component applies a genetic algorithm to avoid an exhaustive search.\",\"PeriodicalId\":234433,\"journal\":{\"name\":\"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITIS.2007.100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2007.100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

分割算法的定量测试意味着对地面真值分割的严格测试。尽管在文献中报道不足,分割算法的性能取决于输入参数的选择。本文报道了一种均值移位算法在评估时间和分割结果上的广泛差异。当测试扩展到算法的参数空间时,寻找令人满意的设置将花费相当多的时间。本文考虑使用遗传算法(GA)来避免穷举搜索。由于遗传算法的应用大大减少了搜索时间,本文从初始候选种群、收敛速度和最终抛光轮的应用等方面研究了如何最好地应用遗传算法。遗传算法参数搜索是三分量计算环境的一部分,其目的是实现搜索的自动化和缩短评估时间。第一个组件依赖于脚本化测试和结果整理。第二个组件传输到商用集群计算机。第三部分应用遗传算法来避免穷举搜索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computationally Efficient Quantitative Testing of Image Segmentation with a Genetic Algorithm
Quantitative testing of segmentation algorithms implies rigorous testing against ground truth segmentations. Though under-reported in the literature, the performance of a segmentation algorithm depends on the choice of input parameters. The paper reports wide variety both in evaluation time and segmentation results for an example mean-shift algorithm. When testing extends over an algorithmpsilas parameter space, then the search for satisfactory settings has a considerable cost in time. This paper considers the use of a genetic algorithm (GA) to avoid an exhaustive search. As application of the GA drastically reduces search times, the paper investigates how best to apply the GA in terms of initial candidate population, convergence speed, and application of a final polishing round. The GA parameter search forms part of a three-component computation environment aimed at automating the search and reducing the evaluation time. The first component relies on scripted testing and collation of results. The second component transfers to a commodity cluster computer. And the third component applies a genetic algorithm to avoid an exhaustive search.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信