{"title":"KPYR:一种高效的索引方法","authors":"T. Urruty, F. Belkouch, C. Djeraba","doi":"10.1109/ICME.2005.1521704","DOIUrl":null,"url":null,"abstract":"Motivated by the needs for efficient indexing structures adapted to real applications in video database, we present a new indexing structure named Kpyr. In Kpyr, we use a clustering algorithm to partition the data space into sub-spaces on which we apply Pyramid technique (S. Berchtold, et al., 1998). We thus reduce the search space concerned by a query and improve the performances. We show that our approach provides interesting and performing experimental results for both K-nearest neighbors and window queries","PeriodicalId":244360,"journal":{"name":"2005 IEEE International Conference on Multimedia and Expo","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"KPYR: An Efficient Indexing Method\",\"authors\":\"T. Urruty, F. Belkouch, C. Djeraba\",\"doi\":\"10.1109/ICME.2005.1521704\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivated by the needs for efficient indexing structures adapted to real applications in video database, we present a new indexing structure named Kpyr. In Kpyr, we use a clustering algorithm to partition the data space into sub-spaces on which we apply Pyramid technique (S. Berchtold, et al., 1998). We thus reduce the search space concerned by a query and improve the performances. We show that our approach provides interesting and performing experimental results for both K-nearest neighbors and window queries\",\"PeriodicalId\":244360,\"journal\":{\"name\":\"2005 IEEE International Conference on Multimedia and Expo\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Conference on Multimedia and Expo\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICME.2005.1521704\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Multimedia and Expo","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICME.2005.1521704","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Motivated by the needs for efficient indexing structures adapted to real applications in video database, we present a new indexing structure named Kpyr. In Kpyr, we use a clustering algorithm to partition the data space into sub-spaces on which we apply Pyramid technique (S. Berchtold, et al., 1998). We thus reduce the search space concerned by a query and improve the performances. We show that our approach provides interesting and performing experimental results for both K-nearest neighbors and window queries
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