Fusion of time series representations for plant recognition in phenology studies

Fusion of time series representations for plant recognition in phenology studies

Author Faria, Fabio Augusto Autor UNIFESP Google Scholar
Almeida, Jurandy Autor UNIFESP Google Scholar
Alberton, Bruna Google Scholar
Morellato, Leonor Patricia C. Google Scholar
Torres, Ricardo da S. Google Scholar
Abstract Nowadays, global warming and its resulting environmental changes is a hot topic in different biology research area. Phenology is one effective way of tracking such environmental changes through the study of plant's periodic events and their relationship to climate. One promising research direction in this area relies on the use of vegetation images to track phenology changes over time. In this scenario, the creation of effective image-based plant identification systems is of paramount importance. In this paper, we propose the use of a new representation of time series to improve plants recognition rates. This representation, called recurrence plot (RP), is a technique for nonlinear data analysis, which represents repeated events on time series into a two-dimensional representation (an image). Therefore, image descriptors can be used to characterize visual properties from this RP images so that these features can be used as input of a classifier. To the best of our knowledge, this is the first work that uses recurrence plot for plant recognition task. Performed experiments show that RP can be a good solution to describe time series. In addition, in a comparison with visual rhythms (VR), another technique used for time series representation, RP shows a better performance to describe texture properties than VR. On the other hand, a correlation analysis and the adoption of a well successful classifier fusion framework show that both representations provide complementary information that is useful for improving classification accuracies. (C) 2016 Elsevier B.V. All rights reserved.
Keywords Plant species identification
Classifier fusion
Diversity measures
xmlui.dri2xhtml.METS-1.0.item-coverage Amsterdam
Language English
Sponsor Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Microsoft Research Virtual Institute
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Grant number FAPESP: 2010/52113-5
FAPESP: 2013/50169-1
FAPESP: 2013/50155-0
FAPESP: 2014/00215-0
FAPESP: 2009/18438-7
FAPESP: 2010/51307-0
CNPq: 310761/2014-0
CNPq: 306580/2012-8
Date 2016
Published in Pattern Recognition Letters. Amsterdam, v. 83, p. 205-214, 2016.
ISSN 0167-8655 (Sherpa/Romeo, impact factor)
Publisher Elsevier Science Bv
Extent 205-214
Origin http://dx.doi.org/10.1016/j.patrec.2016.03.005
Access rights Closed access
Type Article
Web of Science ID WOS:000386874800012
URI https://repositorio.unifesp.br/handle/11600/56850

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