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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Time series-based classifier fusion for fine-grained plant species recognition

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Author(s):
Faria, Fabio A. ; Almeida, Jurandy ; Alberton, Bruna ; Morellato, Leonor Patricia C. ; Rocha, Anderson ; Torres, Ricardo da S.
Total Authors: 6
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 81, n. SI, p. 101-109, OCT 1 2016.
Web of Science Citations: 5
Abstract

Global warming and its resulting environmental changes surely are ubiquitous subjects nowadays and undisputedly important research topics. One way of tracking such environmental changes is by means of phenology, which studies natural periodic events and their relationship to climate. Phenology is seen as the simplest and most reliable indicator of the effects of climate change on plants and animals. The search for phenological information and monitoring systems has stimulated many research centers worldwide to pursue the development of effective and innovative solutions in this direction. One fundamental requirement for phenological systems is concerned with achieving fine-grained recognition of plants. In this sense, the present work seeks to understand specific properties of each target plant species and to provide the solutions for gathering specific knowledge of such plants for further levels of recognition and exploration in related tasks. In this work, we address some important questions such as: (i) how species from the same leaf functional group differ from each other; (ii) how different pattern classifiers might be combined to improve the effectiveness results in target species identification; and (iii) whether it is possible to achieve good classification results with fewer classifiers for fine-grained plant species identification. In this sense, we perform different analysis considering RGB color information channels from a digital hemispherical lens camera in different hours of day and plant species. A study about the correlation of classifiers associated with time series extracted from digital images is also performed. We adopt a successful selection and fusion framework to combine the most suitable classifiers and features improving the plant identification decision-making task as it is nearly impossible to develop just a single ``silver bullet{''} image descriptor that would capture all subtle discriminatory features of plants within the same functional group. This adopted framework turns out to be an effective solution in the target task, achieving better results than well-known approaches in the literature. (C) 2015 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 10/14910-0 - Evidence-Fusion methods for multimedia retrieval and classification
Grantee:Fabio Augusto Faria
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 14/00215-0 - Remote phenology and leaf exchange patterns towards a sazonality gradient
Grantee:Bruna de Costa Alberton
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 10/51307-0 - Floristic diversity and seasonal patterns of rupestrian fields and cerrado
Grantee:Leonor Patricia Cerdeira Morellato
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 07/52015-0 - Approximation methods for visual computing
Grantee:Jorge Stolfi
Support type: Research Projects - Thematic Grants
FAPESP's process: 10/05647-4 - Digital forensics: collection, organization, classification and analysis of digital evidences
Grantee:Anderson de Rezende Rocha
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 09/18438-7 - Large-scale classification and retrieval for complex data
Grantee:Ricardo da Silva Torres
Support type: Regular Research Grants
FAPESP's process: 10/52113-5 - e-phenology: the application of new technologies to monitor plant phenology and track climate changes in the tropics
Grantee:Leonor Patricia Cerdeira Morellato
Support type: Research Program on Global Climate Change - Regular Grants
FAPESP's process: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
Grantee:Leonor Patricia Cerdeira Morellato
Support type: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)