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		<identifier>8JMKD3MGPBW34M/3CA2238</identifier>
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		<citationkey>FariaSantRochTorr:2012:AuClFu</citationkey>
		<title>Automatic Classifier Fusion for Produce Recognition</title>
		<format>DVD, On-line.</format>
		<year>2012</year>
		<numberoffiles>1</numberoffiles>
		<size>1106 KiB</size>
		<author>Faria, Fabio Augusto,</author>
		<author>Santos, Jefersson Alex dos,</author>
		<author>Rocha, Anderson,</author>
		<author>Torres, Ricardo da Silva,</author>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<editor>Freitas, Carla Maria Dal Sasso,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Scopigno, Roberto,</editor>
		<editor>Silva, Luciano,</editor>
		<e-mailaddress>ffaria@ic.unicamp.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 25 (SIBGRAPI)</conferencename>
		<conferencelocation>Ouro Preto</conferencelocation>
		<date>Aug. 22-25, 2012</date>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>Recognition, Ensemble of Classifiers, Diversity Measures.</keywords>
		<abstract>Recognizing different kinds of fruits and vegetables is a common task in supermarkets. This task, however, poses several challenges as it requires the identification of different species of a particular produce and also its variety. Usually, existing computer-based recognition approaches are not automatic and demand long-term and laborious prior training sessions. This paper presents a novel framework for classifier fusion aiming at supporting the automatic recognition of fruits and vegetables in a supermarket environment. The objective is to provide an effective mechanism for combining low-cost classifiers trained for specific classes of interest. The experiments performed demonstrate that the proposed framework yields better results than several related work found in the literature and represents a step forward automatic produce recognition in cashiers of supermarkets.</abstract>
		<language>en</language>
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