{"title":"使用语义增强的图像标题NLP来辅助图像检索","authors":"Kraisak Kesorn, S. Poslad","doi":"10.1109/SMAP.2008.18","DOIUrl":null,"url":null,"abstract":"This paper proposes a semantic-based create and search technique to enhance visual information retrieval. Our approach includes an ontology-based scheme for the semi-automatic annotation for image retrieval. Latent Semantic Indexing (LSI) is used in order to solve the Natural Language (NL) vagueness problem and to tolerate ontology imperfections. In addition, our framework is able to find indirect relevant concepts in images and to represent image semantics at a higher level. Experiments demonstrate that semantic-based approaches can significantly improve image retrieval.","PeriodicalId":292389,"journal":{"name":"2008 Third International Workshop on Semantic Media Adaptation and Personalization","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Use of Semantic Enhancements to NLP of Image Captions to Aid Image Retrieval\",\"authors\":\"Kraisak Kesorn, S. Poslad\",\"doi\":\"10.1109/SMAP.2008.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a semantic-based create and search technique to enhance visual information retrieval. Our approach includes an ontology-based scheme for the semi-automatic annotation for image retrieval. Latent Semantic Indexing (LSI) is used in order to solve the Natural Language (NL) vagueness problem and to tolerate ontology imperfections. In addition, our framework is able to find indirect relevant concepts in images and to represent image semantics at a higher level. Experiments demonstrate that semantic-based approaches can significantly improve image retrieval.\",\"PeriodicalId\":292389,\"journal\":{\"name\":\"2008 Third International Workshop on Semantic Media Adaptation and Personalization\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Third International Workshop on Semantic Media Adaptation and Personalization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMAP.2008.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Third International Workshop on Semantic Media Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMAP.2008.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Use of Semantic Enhancements to NLP of Image Captions to Aid Image Retrieval
This paper proposes a semantic-based create and search technique to enhance visual information retrieval. Our approach includes an ontology-based scheme for the semi-automatic annotation for image retrieval. Latent Semantic Indexing (LSI) is used in order to solve the Natural Language (NL) vagueness problem and to tolerate ontology imperfections. In addition, our framework is able to find indirect relevant concepts in images and to represent image semantics at a higher level. Experiments demonstrate that semantic-based approaches can significantly improve image retrieval.