dc.creatorPedronette D.C.G.
dc.creatorPenatti O.A.B.
dc.creatorCalumby R.T.
dc.creatorDa S. Torres R.
dc.date2014
dc.date2015-06-25T18:00:04Z
dc.date2015-11-26T14:59:33Z
dc.date2015-06-25T18:00:04Z
dc.date2015-11-26T14:59:33Z
dc.date.accessioned2018-03-28T22:11:06Z
dc.date.available2018-03-28T22:11:06Z
dc.identifier
dc.identifierIcmr 2014 - Proceedings Of The Acm International Conference On Multimedia Retrieval 2014. Association For Computing Machinery, v. , n. , p. 345 - 352, 2014.
dc.identifier
dc.identifier10.1145/2578726.2578770
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84899769548&partnerID=40&md5=25c4854cdf0578d383c54490f03dbe7a
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87387
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87387
dc.identifier2-s2.0-84899769548
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1256070
dc.descriptionThis paper presents a novel unsupervised learning approach that takes into account the intrinsic dataset structure, which is represented in terms of the reciprocal neighborhood references found in different ranked lists. The proposed Reciprocal kNN Distance defines a more effective distance between two images, and is used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach is also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of proposed approach. The Reciprocal kNN Distance yields better results in terms of effectiveness than various state-of-the-art algorithms. Copyright © 2014 ACM.
dc.description
dc.description
dc.description345
dc.description352
dc.descriptionAMD; Advanced Micro Devices; Advanced Micro Devices
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dc.languageen
dc.publisherAssociation for Computing Machinery
dc.relationICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014
dc.rightsfechado
dc.sourceScopus
dc.titleUnsupervised Distance Learning By Reciprocal Knn Distance For Image Retrieval
dc.typeActas de congresos


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