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Data efficient methods for plankton image classification

Grant number: 20/15170-2
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): January 01, 2021
Effective date (End): May 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Acordo de Cooperação: Belmont Forum
Principal Investigator:Nina Sumiko Tomita Hirata
Grantee:Antonio Jose Homsi Goulart
Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:18/24167-5 - World Wide Web of Plankton Image Curation (www.pic), AP.R

Abstract

The main goal of this postdoc project is to develop machine learning based computational methods to speed up both annotation and classification of plankton images, minimizing the required effort from the expert. The method should be able to effectively reuse previously generated knowledge, quickly producing new classifiers adapted to new imaging or use conditions. Unsupervised and semi-supervised machine learning approaches, possibly including user-interaction mechanisms, as well as techniques related to novelty detection and class imbalance treatment, are some of the research topics to be explored.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
HOMSI GOULART, ANTONIO JOSE; MORIMITSU, ALEXANDRE; JACOMASSI, RENAN; HIRATA, NINA; LOPES, RUBENS; IEEE. Deep learning and t-SNE projection for plankton images clusterization. OCEANS 2021: SAN DIEGO - PORTO, v. N/A, p. 4-pg., . (21/02902-8, 20/15170-2)

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