{"title":"Efficient Shape Retrieval Under Partial Matching","authors":"M. Demirci","doi":"10.1109/ICPR.2010.749","DOIUrl":null,"url":null,"abstract":"Indexing into large database systems is essential for a number of applications. This paper presents a new indexing structure, which overcomes an important restriction of a previous indexing technique using a recently developed theorem from the domain of matrix analysis. Specifically, given a set of distance values computed by distance function, which do not necessarily satisfy the triangle inequality, this paper shows that computing its nearest distance values that obey the properties of a metric enables us to overcome the limitations of the previous indexing algorithm. We demonstrate the proposed framework in the context of a recognition task.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 20th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2010.749","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Indexing into large database systems is essential for a number of applications. This paper presents a new indexing structure, which overcomes an important restriction of a previous indexing technique using a recently developed theorem from the domain of matrix analysis. Specifically, given a set of distance values computed by distance function, which do not necessarily satisfy the triangle inequality, this paper shows that computing its nearest distance values that obey the properties of a metric enables us to overcome the limitations of the previous indexing algorithm. We demonstrate the proposed framework in the context of a recognition task.