{"title":"LB-Index:图像的多分辨率索引结构","authors":"Vebjorn Ljosa, Arnab Bhattacharya, Ambuj K. Singh","doi":"10.1109/ICDE.2006.85","DOIUrl":null,"url":null,"abstract":"In many domains, the similarity between two images depends on the spatial locations of their features. The earth mover’s distance (EMD), first proposed by Werman et al. [8], measures such similarity. It yields higher-quality image retrieval results than the Lp-norm, quadratic-form distance, and Jeffrey divergence [6], and has also been used for similarity search on contours [3], melodies [7], and graphs [2].","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"22 1","pages":"144-144"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"LB-Index: A Multi-Resolution Index Structure for Images\",\"authors\":\"Vebjorn Ljosa, Arnab Bhattacharya, Ambuj K. Singh\",\"doi\":\"10.1109/ICDE.2006.85\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many domains, the similarity between two images depends on the spatial locations of their features. The earth mover’s distance (EMD), first proposed by Werman et al. [8], measures such similarity. It yields higher-quality image retrieval results than the Lp-norm, quadratic-form distance, and Jeffrey divergence [6], and has also been used for similarity search on contours [3], melodies [7], and graphs [2].\",\"PeriodicalId\":6819,\"journal\":{\"name\":\"22nd International Conference on Data Engineering (ICDE'06)\",\"volume\":\"22 1\",\"pages\":\"144-144\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd International Conference on Data Engineering (ICDE'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2006.85\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference on Data Engineering (ICDE'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2006.85","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
LB-Index: A Multi-Resolution Index Structure for Images
In many domains, the similarity between two images depends on the spatial locations of their features. The earth mover’s distance (EMD), first proposed by Werman et al. [8], measures such similarity. It yields higher-quality image retrieval results than the Lp-norm, quadratic-form distance, and Jeffrey divergence [6], and has also been used for similarity search on contours [3], melodies [7], and graphs [2].
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