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		<citationkey>PessoaSchwSant:2016:LoViFe</citationkey>
		<title>Low-cost visual feature representations for image retrieval</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
		<size>840 KiB</size>
		<author>Pessoa, Ramon Figueiredo,</author>
		<author>Schwartz, William Robson,</author>
		<author>dos Santos, Jefersson Alex,</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>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>ramon.pessoa@dcc.ufmg.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>image search, global descriptors, binary descriptors, bag of visual words, spatial bag of visual words.</keywords>
		<abstract>This work addressed two research issues in order to investigate and to propose effective solutions for image retrieval on mobile devices: 1) low-cost representation for mobile image search and 2) spatial visual feature extraction. First, we test twenty mid-level representations of binary descriptors, ten color descriptors, five texture descriptors and two shape descriptors in ten datasets, considering the trade-off configuration regarding effectiveness, efficiency, and compactness of visual features. Finally, we propose two approaches of spatial bags of visual words called BOBGrid (spatial Bag Of BIC Grid) and BOBSlic (spatial Bag Of Slic) and compare them with our baselines. In statistical analyzes, BOBGrid and BOBSlic achieved processing results and performed better than our baselines WSA and BOSSANova.</abstract>
		<language>en</language>
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		<usergroup>ramon.pessoa@dcc.ufmg.br</usergroup>
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