Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning
Hansang Lee, Minseok Park, Junmo Kim · 2016
Plankton image classification plays an important role in the ocean ecosystems research. Recently, a large scale database for plankton classification with over 3 million images annotated with over 100 classes was released. However, the database suffers from imbalanced class distribution in which over 90% of images belong to only 5 classes. Due to this class-imbalance problem, the existing classification approaches are limited to label the data only to major classes, ignoring the small-sized classes. In this paper, we propose a fine-grained classification method for large scale plankton database based on convolutional neural networks (CNN). To overcome the class-imbalance problem, we incorporate transfer learning by pre-training CNN with class-normalized data and fine-tuning with original data. The class-normalized data is constructed by reducing the number of data via random sampling, for large-sized classes. In experiments, our method showed superior classification accuracy compared to both CNN without transfer learning and CNN with transfer learning via other data augmentation techniques.