Automatic fruit and vegetable classification from images

dc.creatorRocha, A
dc.creatorHauagge, DC
dc.creatorWainer, J
dc.creatorGoldenstein, S
dc.date2010
dc.dateJAN
dc.date2014-11-17T07:03:40Z
dc.date2015-11-26T17:01:31Z
dc.date2014-11-17T07:03:40Z
dc.date2015-11-26T17:01:31Z
dc.date.accessioned2018-03-28T23:49:20Z
dc.date.available2018-03-28T23:49:20Z
dc.identifierComputers And Electronics In Agriculture. Elsevier Sci Ltd, v. 70, n. 1, n. 96, n. 104, 2010.
dc.identifier0168-1699
dc.identifierWOS:000273933600011
dc.identifier10.1016/j.compag.2009.09.002
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/55214
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/55214
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/55214
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1278740
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionContemporary Vision and Pattern Recognition problems such as face recognition, fingerprinting identification, image categorization, and DNA sequencing often have an arbitrarily large number of classes and properties to consider. To deal with such complex problems using just one feature descriptor is a difficult task and feature fusion may become mandatory. Although normal feature fusion is quite effective for some problems. it can yield unexpected classification results when the different features are not properly normalized and preprocessed. Besides it has the drawback of increasing the dimensionality which might require more training data. To cope with these problems, this paper introduces a unified approach that can combine many features and classifiers that requires less training and is more adequate to some problems than a naive method, where all features are simply concatenated and fed independently to each classification algorithm. Besides that, the presented technique is amenable to continuous learning, both when refining a learned model and also when adding new classes to be discriminated. The introduced fusion approach is validated using a multi-class fruit-and-vegetable categorization task in a semi-controlled environment, such as a distribution center or the supermarket cashier. The results show that the solution is able to reduce the classification error in up to 15 percentage points with respect to the baseline. (C) 2009 Elsevier B.V. All rights reserved.
dc.description70
dc.description1
dc.description96
dc.description104
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFAPESP [2008/08681-9, 05/58103-3, 07/52015-0, 08/54443-2]
dc.descriptionCNPq [309254/2007-8, 472402/2007-2, 551007/2007-9]
dc.languageen
dc.publisherElsevier Sci Ltd
dc.publisherOxford
dc.publisherInglaterra
dc.relationComputers And Electronics In Agriculture
dc.relationComput. Electron. Agric.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectFeature and classifier fusion
dc.subjectMulti-class from binary
dc.subjectAutomatic produce classification
dc.subjectImage classification
dc.titleAutomatic fruit and vegetable classification from images
dc.typeArtículos de revistas


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