{"title":"基于相似度的时态文档检索","authors":"P. Yamuna, K. Candan","doi":"10.1145/357744.357955","DOIUrl":null,"url":null,"abstract":"In this paper, we describe a similarity-based retrieval framework that addresses the challenges associated with the temporal nature of multimedia documents. Multimedia documents consist of multiple media objects and a set of specifications (eg. temporal) that tie these objects together. Therefore, we describe similarity/dissimilarity measures that aim to capture document authors' intensions. We use a prioritized constraint-based framework to evaluate these measures. We also develop algorithms that efficiently compute these measures for special cases.","PeriodicalId":234597,"journal":{"name":"MULTIMEDIA '00","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Similarity-based retrieval of temporal documents\",\"authors\":\"P. Yamuna, K. Candan\",\"doi\":\"10.1145/357744.357955\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe a similarity-based retrieval framework that addresses the challenges associated with the temporal nature of multimedia documents. Multimedia documents consist of multiple media objects and a set of specifications (eg. temporal) that tie these objects together. Therefore, we describe similarity/dissimilarity measures that aim to capture document authors' intensions. We use a prioritized constraint-based framework to evaluate these measures. We also develop algorithms that efficiently compute these measures for special cases.\",\"PeriodicalId\":234597,\"journal\":{\"name\":\"MULTIMEDIA '00\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MULTIMEDIA '00\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/357744.357955\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MULTIMEDIA '00","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/357744.357955","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we describe a similarity-based retrieval framework that addresses the challenges associated with the temporal nature of multimedia documents. Multimedia documents consist of multiple media objects and a set of specifications (eg. temporal) that tie these objects together. Therefore, we describe similarity/dissimilarity measures that aim to capture document authors' intensions. We use a prioritized constraint-based framework to evaluate these measures. We also develop algorithms that efficiently compute these measures for special cases.