| Full text | |
| Author(s): |
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
Total Authors: 9
|
| Document type: | Journal article |
| Source: | 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. |
| 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. (AU) | |
| FAPESP's process: | 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications |
| Grantee: | João Eduardo Ferreira |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Thematic Grants |
| FAPESP's process: | 13/17633-6 - Plankton detection in the surface ocean by high-resolution sensor technology |
| Grantee: | Rubens Mendes Lopes |
| Support Opportunities: | Regular Research Grants |