{"title":"AutoAlbum:使用概率模型合并聚类数字照片","authors":"Microsoft Way Redmond","doi":"10.1109/IVL.2000.853847","DOIUrl":null,"url":null,"abstract":"Consumers need help finding digital photographs in their personal collections. AutoAlbum helps users find their photos by automatically clustering photos into an album. The albums are presented in an easy-to-use browsing user interface. AutoAlbum uses the time and order of photo creation to assist in clustering: albums consist of temporally contiguous photos. The content based clustering algorithm is best-first probabilistic model merging, which is fast and yields clusters that are often semantically meaningful.","PeriodicalId":333664,"journal":{"name":"2000 Proceedings Workshop on Content-based Access of Image and Video Libraries","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"137","resultStr":"{\"title\":\"AutoAlbum: clustering digital photographs using probabilistic model merging\",\"authors\":\"Microsoft Way Redmond\",\"doi\":\"10.1109/IVL.2000.853847\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Consumers need help finding digital photographs in their personal collections. AutoAlbum helps users find their photos by automatically clustering photos into an album. The albums are presented in an easy-to-use browsing user interface. AutoAlbum uses the time and order of photo creation to assist in clustering: albums consist of temporally contiguous photos. The content based clustering algorithm is best-first probabilistic model merging, which is fast and yields clusters that are often semantically meaningful.\",\"PeriodicalId\":333664,\"journal\":{\"name\":\"2000 Proceedings Workshop on Content-based Access of Image and Video Libraries\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"137\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2000 Proceedings Workshop on Content-based Access of Image and Video Libraries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVL.2000.853847\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2000 Proceedings Workshop on Content-based Access of Image and Video Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVL.2000.853847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AutoAlbum: clustering digital photographs using probabilistic model merging
Consumers need help finding digital photographs in their personal collections. AutoAlbum helps users find their photos by automatically clustering photos into an album. The albums are presented in an easy-to-use browsing user interface. AutoAlbum uses the time and order of photo creation to assist in clustering: albums consist of temporally contiguous photos. The content based clustering algorithm is best-first probabilistic model merging, which is fast and yields clusters that are often semantically meaningful.