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Reference TypeConference Proceedings
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyAlmeidaJrGuim:2018:CaStHu
TitleExploring Feature Distribution to Create Mid-level Representations: A Case Study in Human Action Recognition
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size1231 KiB
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Author1 Almeida, Raquel
2 Jr. , Zenilton K. G. do Pratrocínio
3 Guimarães, Silvio Jamil F.
Affiliation1 Pontifical Catholic University of Minas Gerais
2 Pontifical Catholic University of Minas Gerais
3 Pontifical Catholic University of Minas Gerais
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-18 12:41:19 :: -> administrator ::
2020-02-20 22:06:50 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
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Tertiary TypeMaster's or Doctoral Work
Keywordsdata representation, mid-level, bag-of-words, human action recognition.
AbstractData representation is a critical task in many areas of computational studies, particularly in the case of visual data representation, in which subtleties can undermine the perception and interpretation of the visual content. In this study, it is proposed strategies to exploit visual mid-level representations, aiming to transform the detailed description extracted directly from the visual media into a simplified and discriminative representation. More specifically, the proposed strategies are delineated in Bag-of-Words mid-level representation model and are used to aggregate distribution information within partitions and regions of interest on feature space. Experiments on three well-known public datasets, namely, KTH, UCF Sports and UCF 11, demonstrated that feature points spatial distribution information is useful to create more discriminative representations. All three proposed representations were published and outperform, in terms of recognition rate, conventional strategies on BoW model and are, in many cases, superior or comparable with the state-of-the-art.
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