Pr: More Than Meets The Eye

dc.creatorRocha A.
dc.creatorGoldenstein S.
dc.date2007
dc.date2015-06-30T18:40:17Z
dc.date2015-11-26T14:31:12Z
dc.date2015-06-30T18:40:17Z
dc.date2015-11-26T14:31:12Z
dc.date.accessioned2018-03-28T21:34:34Z
dc.date.available2018-03-28T21:34:34Z
dc.identifier
dc.identifierProceedings Of The Ieee International Conference On Computer Vision. , v. , n. , p. - , 2007.
dc.identifier
dc.identifier10.1109/ICCV.2007.4408921
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-50649107656&partnerID=40&md5=25f66d9f611597d829df743691407b58
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/104264
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/104264
dc.identifier2-s2.0-50649107656
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1247310
dc.descriptionIn this paper, we introduce a new image descriptor for broad Image Categorization, the Progressive Randomization (PR), that uses perturbations on the values of the Least Significant Bits (LSB) of images. We show that different classes of images have a distinct behavior under our methodology, and that using statistical descriptors of LSB occurrences and enough training examples, the method already performs as well or better than comparable existing techniques in the literature. With few training examples, PR still has good separability, and its accuracy increases with the size of the training set. We validate our method using four image databases with different categories. ©2007 IEEE.
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dc.languageen
dc.publisher
dc.relationProceedings of the IEEE International Conference on Computer Vision
dc.rightsfechado
dc.sourceScopus
dc.titlePr: More Than Meets The Eye
dc.typeActas de congresos


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