Evaluation of Transfer Learning Scenarios in Plankton Image Classification

Autor(es)

Maia Rodrigues, Francisco Caio ; Hirata, Nina S. T. ; Abello, Antonio A. ; De La Cruz, Leandro T. ; Lopes, Rubens M. ; Hirata Jr, R. ; Imai, F ; Tremeau, A ; Braz, J

• Tipo de documento: Artigo Científico
• Fonte: VISAPP: PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL 4: VISAPP; v. N/A, p. 8-pg., 2018-01-01.

Resumo

Automated in situ plankton image classification is a challenging task. To take advantage of recent progress in machine learning techniques, a large amount of labeled data is necessary. However, beyond being time consuming, labeling is a task that may require frequent redoing due to variations in plankton population as well as image characteristics. Transfer learning, which is a machine learning technique concerned with transferring knowledge obtained in some data domain to a second distinct data domain, appears as a potential approach to be employed in this scenario. We use convolutional neural networks, trained on publicly available distinct datasets, to extract features from our plankton image data and then train SVM classifiers to perform the classification. Results show evidences that indicate the effectiveness of transfer learning in real plankton image classification situations. (AU)

• Processo FAPESP: 15/01587-0 - Armazenagem, modelagem e análise de sistemas dinâmicos para aplicações em e-Science
• Beneficiário: João Eduardo Ferreira
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Temático
• Processo FAPESP: 13/17633-6 - Detecção do plâncton no oceano superficial por tecnologia de sensores de alta resolução
• Beneficiário: Rubens Mendes Lopes
• Modalidade de apoio: Auxílio à Pesquisa - Regular