Elena Jakubiak Hutchinson, Sarah F. Frisken, R. N. Perry
{"title":"Proximity Cluster Trees","authors":"Elena Jakubiak Hutchinson, Sarah F. Frisken, R. N. Perry","doi":"10.1080/2151237X.2008.10129256","DOIUrl":null,"url":null,"abstract":"Hierarchical spatial data structures provide a means for organizing data for efficient processing. Most spatial data structures are optimized for performing queries, such as intersection and containment testing, on large data sets. Set-up time and complexity of these structures can limit their value for small data sets, an often overlooked yet important category in geometric processing. We present a new hierarchical spatial data structure, dubbed a proximity cluster tree, which is particularly effective on small data sets. Proximity cluster trees are simple to implement, require minimal construction overhead, and are structured for fast distance-based queries. Proximity cluster trees were tested on randomly generated sets of 2D Bézier curves and on a text-rendering application requiring minimum-distance queries to 2D glyph outlines. Although proximity cluster trees were tailored for small data sets, empirical tests show that they also perform well on large data sets.","PeriodicalId":318334,"journal":{"name":"Journal of Graphics Tools","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Graphics Tools","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/2151237X.2008.10129256","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Hierarchical spatial data structures provide a means for organizing data for efficient processing. Most spatial data structures are optimized for performing queries, such as intersection and containment testing, on large data sets. Set-up time and complexity of these structures can limit their value for small data sets, an often overlooked yet important category in geometric processing. We present a new hierarchical spatial data structure, dubbed a proximity cluster tree, which is particularly effective on small data sets. Proximity cluster trees are simple to implement, require minimal construction overhead, and are structured for fast distance-based queries. Proximity cluster trees were tested on randomly generated sets of 2D Bézier curves and on a text-rendering application requiring minimum-distance queries to 2D glyph outlines. Although proximity cluster trees were tailored for small data sets, empirical tests show that they also perform well on large data sets.