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
Keyword(s):
Transfer Learning, Plankton Classification, CNN, SVM, Alexnet, ImageNet.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
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