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		<citationkey>PessoaSchwSant:2015:ExCoFe</citationkey>
		<title>An experimental comparison of feature extraction and distance metrics for image retrieval</title>
		<format>On-line</format>
		<year>2015</year>
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		<author>Pessoa, Ramon Figueiredo,</author>
		<author>Schwartz, William Robson,</author>
		<author>Santos, Jefersson Alex dos,</author>
		<affiliation>Universidade Federal de Minas Gerais (UFMG)</affiliation>
		<affiliation>Universidade Federal de Minas Gerais (UFMG)</affiliation>
		<affiliation>Universidade Federal de Minas Gerais (UFMG)</affiliation>
		<editor>Rios, Ricardo Araujo,</editor>
		<editor>Paiva, Afonso,</editor>
		<e-mailaddress>ramon.pessoa@dcc.ufmg.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador, BA, Brazil</conferencelocation>
		<date>26-29 Aug. 2015</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Work in Progress</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>CBIR experimental comparison, statistical analysis, feature extraction algorithms, distance metrics.</keywords>
		<abstract>This paper seeks to do a comparative study of different features and distance metrics in order to analyze the impact of these factors in the process of Content-Based Image Retrieval (CBIR). One of the main contributions of this work was statistically analyze the impact of distance metrics in the process of image retrieval by content. We also observed statistically the impact of the variability among different classes and also the variability between images of the same image class. The results showed, for a sample collected, that the variation attributed to the class is approximately 99.85%. This confirms the fact that each algorithm will work best in a given situation. The comparative study showed the algorithms which had better accuracy rate to recover different image classes (in the dataset analysed) and also presented the reasons that possibly made these algorithms had better accuracy rate.</abstract>
		<language>en</language>
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