Image Categorization Through Optimum Path Forest And Visual Words

dc.creatorPapa J.P.
dc.creatorRocha A.
dc.date2011
dc.date2015-06-30T20:18:16Z
dc.date2015-11-26T14:47:43Z
dc.date2015-06-30T20:18:16Z
dc.date2015-11-26T14:47:43Z
dc.date.accessioned2018-03-28T21:58:18Z
dc.date.available2018-03-28T21:58:18Z
dc.identifier9781457713033
dc.identifierProceedings - International Conference On Image Processing, Icip. , v. , n. , p. 3525 - 3528, 2011.
dc.identifier15224880
dc.identifier10.1109/ICIP.2011.6116475
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84856297857&partnerID=40&md5=5f2b949e1b6c3135ec2eabbd2aa2f13d
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/107507
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/107507
dc.identifier2-s2.0-84856297857
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1253393
dc.descriptionDifferent from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.
dc.description
dc.description
dc.description3525
dc.description3528
dc.description IEEE,IEEE Signal Processing Society
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dc.languageen
dc.publisher
dc.relationProceedings - International Conference on Image Processing, ICIP
dc.rightsfechado
dc.sourceScopus
dc.titleImage Categorization Through Optimum Path Forest And Visual Words
dc.typeActas de congresos


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