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		<doi>10.1109/SIBGRAPI.2016.059</doi>
		<citationkey>RibeiroTeiFerJrNas:2016:CoAsLa</citationkey>
		<title>Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition</title>
		<format>On-line</format>
		<year>2016</year>
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		<author>Ribeiro, Manoel Horta,</author>
		<author>Teixeira, Bruno,</author>
		<author>Fernandes, Antônio Otávio,</author>
		<author>Jr. , Wagner Meira,</author>
		<author>Nascimento, Erickson R.,</author>
		<affiliation>Computer Science Department, Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Computer Science Department, Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Computer Science Department, Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Computer Science Department, Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Computer Science Department, Universidade Federal de Minas Gerais</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>manoelribeiro@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>IEEE Computer Society´s Conference Publishing Services</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>discriminative models, conditional random fields, gesture recognition, activity recognition.</keywords>
		<abstract>Many of the state-of-the-art algorithms for gesture recognition are based on Conditional Random Fields (CRFs). Successful approaches, such as the Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose values are mapped to the values of the labels. In this paper we propose a novel methodology to set the latent values according to the gesture complexity. We use an heuristic that iterates through the samples associated with each label value, estimating their complexity. We then use it to assign the latent values to the label values. We evaluate our method on the task of recognizing human gestures from video streams. The experiments were performed in binary datasets, generated by grouping different labels. Our results demonstrate that our approach outperforms the arbitrary one in many cases, increasing the accuracy by up to 10\%.</abstract>
		<language>en</language>
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		<usergroup>manoelribeiro@dcc.ufmg.br</usergroup>
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