{"title":"多媒体数据库浏览系统","authors":"Massimiliano Albanese, C. Cesarano, A. Picariello","doi":"10.1145/1039470.1039481","DOIUrl":null,"url":null,"abstract":"Browsing large multimedia databases is becoming a challenging problem, due to the availability of great amounts of data and the complexity of retrieval. In this paper we propose a system that assists a user in browsing a digital collection making useful recommendations. The system combines computer vision techniques and taxonomic classifcations to measure the similarity between objects and adopts an innovative strategy to take into account user behavior.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A multimedia data base browsing system\",\"authors\":\"Massimiliano Albanese, C. Cesarano, A. Picariello\",\"doi\":\"10.1145/1039470.1039481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Browsing large multimedia databases is becoming a challenging problem, due to the availability of great amounts of data and the complexity of retrieval. In this paper we propose a system that assists a user in browsing a digital collection making useful recommendations. The system combines computer vision techniques and taxonomic classifcations to measure the similarity between objects and adopts an innovative strategy to take into account user behavior.\",\"PeriodicalId\":346313,\"journal\":{\"name\":\"Computer Vision meets Databases\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Vision meets Databases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1039470.1039481\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision meets Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1039470.1039481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Browsing large multimedia databases is becoming a challenging problem, due to the availability of great amounts of data and the complexity of retrieval. In this paper we propose a system that assists a user in browsing a digital collection making useful recommendations. The system combines computer vision techniques and taxonomic classifcations to measure the similarity between objects and adopts an innovative strategy to take into account user behavior.
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