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Authors: Francisco Caio Maia Rodrigues ; Nina S. T. Hirata ; Antonio A. Abello ; Leandro T. De La Cruz ; Rubens M. Lopes and R. Hirata Jr.

Affiliation: University of São Paulo, Brazil

ISBN: 978-989-758-290-5

Keyword(s): Transfer Learning, Plankton Classification, CNN, SVM, Alexnet, ImageNet.

Abstract: 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

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Paper citation in several formats:
Rodrigues, F.; Hirata, N.; Abello, A.; De La Cruz, L.; Lopes, R. and Hirata Jr., R. (2018). Evaluation of Transfer Learning Scenarios in Plankton Image Classification.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-290-5, pages 359-366. DOI: 10.5220/0006626703590366

@conference{visapp18,
author={Francisco Caio Maia Rodrigues. and Nina S. T. Hirata. and Antonio A. Abello. and Leandro T. De La Cruz. and Rubens M. Lopes. and R. Hirata Jr..},
title={Evaluation of Transfer Learning Scenarios in Plankton Image Classification},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2018},
pages={359-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006626703590366},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Evaluation of Transfer Learning Scenarios in Plankton Image Classification
SN - 978-989-758-290-5
AU - Rodrigues, F.
AU - Hirata, N.
AU - Abello, A.
AU - De La Cruz, L.
AU - Lopes, R.
AU - Hirata Jr., R.
PY - 2018
SP - 359
EP - 366
DO - 10.5220/0006626703590366

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