{"title":"Automatic Image Annotation based-on Rough Set Theory with Visual Keys","authors":"Manabu Serata, Yutaka Hatakeyama, Kaoru Hirota","doi":"10.1109/ISPACS.2006.364713","DOIUrl":null,"url":null,"abstract":"For automatic image annotation, a method based on rough sets with visual keys is proposed. Using rough set theory the method constructs decision rules about each visual key used for image indexing and about keywords from training set of already annotated images. Then target image is annotated according to constructed decision rules about visual keys which the target image is indexed by. The method is evaluated with training sets of 900 images and with test sets of 100 images on 1,000 manually annotated images in COREL database. Experiments show that recall rates tend to rise easily compared with precision rates on image retrieval with query-by-keywords","PeriodicalId":178644,"journal":{"name":"2006 International Symposium on Intelligent Signal Processing and Communications","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Symposium on Intelligent Signal Processing and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS.2006.364713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
For automatic image annotation, a method based on rough sets with visual keys is proposed. Using rough set theory the method constructs decision rules about each visual key used for image indexing and about keywords from training set of already annotated images. Then target image is annotated according to constructed decision rules about visual keys which the target image is indexed by. The method is evaluated with training sets of 900 images and with test sets of 100 images on 1,000 manually annotated images in COREL database. Experiments show that recall rates tend to rise easily compared with precision rates on image retrieval with query-by-keywords
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