{"title":"基于内容的图像检索的层次聚类关联反馈","authors":"Ionut Mironica, B. Ionescu, C. Vertan","doi":"10.1109/CBMI.2012.6269811","DOIUrl":null,"url":null,"abstract":"In this paper we address the issue of relevance feedback in the context of content-based image retrieval. We propose a method that uses an hierarchical cluster representation of the relevant and non-relevant images in a query. The main advantage of this strategy is in performing on the initial set of the retrieved images (user feedback is provided only once for a small number of retrieved images) instead of performing additional queries as most approaches do. Experimental tests conducted on several standard image databases and using state-of-the-art content descriptors (e.g. MPEG-7, SURF) show that the proposed method provides a significant improvement in the retrieval performance, outperforming some other classic approaches.","PeriodicalId":120769,"journal":{"name":"2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)","volume":"04 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Hierarchical clustering relevance feedback for content-based image retrieval\",\"authors\":\"Ionut Mironica, B. Ionescu, C. Vertan\",\"doi\":\"10.1109/CBMI.2012.6269811\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we address the issue of relevance feedback in the context of content-based image retrieval. We propose a method that uses an hierarchical cluster representation of the relevant and non-relevant images in a query. The main advantage of this strategy is in performing on the initial set of the retrieved images (user feedback is provided only once for a small number of retrieved images) instead of performing additional queries as most approaches do. Experimental tests conducted on several standard image databases and using state-of-the-art content descriptors (e.g. MPEG-7, SURF) show that the proposed method provides a significant improvement in the retrieval performance, outperforming some other classic approaches.\",\"PeriodicalId\":120769,\"journal\":{\"name\":\"2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)\",\"volume\":\"04 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMI.2012.6269811\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMI.2012.6269811","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hierarchical clustering relevance feedback for content-based image retrieval
In this paper we address the issue of relevance feedback in the context of content-based image retrieval. We propose a method that uses an hierarchical cluster representation of the relevant and non-relevant images in a query. The main advantage of this strategy is in performing on the initial set of the retrieved images (user feedback is provided only once for a small number of retrieved images) instead of performing additional queries as most approaches do. Experimental tests conducted on several standard image databases and using state-of-the-art content descriptors (e.g. MPEG-7, SURF) show that the proposed method provides a significant improvement in the retrieval performance, outperforming some other classic approaches.