{"title":"一种新的基于语义的图像检索方法","authors":"A. Lakdashti, M. Moin, K. Badie","doi":"10.1109/ICACT.2008.4493928","DOIUrl":null,"url":null,"abstract":"In this paper, we design a fuzzy system for image retrieval to reduce the semantic gap in the content-based image retrieval systems. Our main contribution is three-fold: (1) designing a fuzzy modeling approach to model the expert human behavior in the image retrieval task, (2) a fuzzy system for semantic-based image retrieval, and (3) a training algorithm for creating the fuzzy rules. The proposed solution not only is a novel idea in the semantic-based image retrieval field, but has enough potential in learning semantics from the user and making a powerful approach to improve the performance of CBIR systems, as our experiments on a set of 2000 images supports our claim.","PeriodicalId":448615,"journal":{"name":"2008 10th International Conference on Advanced Communication Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"A Novel Semantic-Based Image Retrieval Method\",\"authors\":\"A. Lakdashti, M. Moin, K. Badie\",\"doi\":\"10.1109/ICACT.2008.4493928\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we design a fuzzy system for image retrieval to reduce the semantic gap in the content-based image retrieval systems. Our main contribution is three-fold: (1) designing a fuzzy modeling approach to model the expert human behavior in the image retrieval task, (2) a fuzzy system for semantic-based image retrieval, and (3) a training algorithm for creating the fuzzy rules. The proposed solution not only is a novel idea in the semantic-based image retrieval field, but has enough potential in learning semantics from the user and making a powerful approach to improve the performance of CBIR systems, as our experiments on a set of 2000 images supports our claim.\",\"PeriodicalId\":448615,\"journal\":{\"name\":\"2008 10th International Conference on Advanced Communication Technology\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 10th International Conference on Advanced Communication Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACT.2008.4493928\",\"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 10th International Conference on Advanced Communication Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACT.2008.4493928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we design a fuzzy system for image retrieval to reduce the semantic gap in the content-based image retrieval systems. Our main contribution is three-fold: (1) designing a fuzzy modeling approach to model the expert human behavior in the image retrieval task, (2) a fuzzy system for semantic-based image retrieval, and (3) a training algorithm for creating the fuzzy rules. The proposed solution not only is a novel idea in the semantic-based image retrieval field, but has enough potential in learning semantics from the user and making a powerful approach to improve the performance of CBIR systems, as our experiments on a set of 2000 images supports our claim.
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