Deep learning and transfer learning features for plankton classification

Plankton are the most fundamental components of ocean ecosystems, due to their function at many levels of the oceans food chain. Studying and monitoring plankton distribution is vital for global climate and environment protection. Currently, much research is concentrated on the automated recognition of plankton and several imaging-based technologies have been developed for collecting plankton images continuously using underwater image sensors. In this paper, we present a study about an automated plankton recognition system, which is based on the fusion of different deep learning methods. In this work we study both the fine tuning of several deep learned models and transfer learning from the same models with the aim of exploiting their diversity in designing an ensemble of classifiers, we deal with: (i) the ability of fine-tuning pre-trained CNN for plankton classification, (ii) the possibility of using pre-trained CNN for transfer learning, (iii) the possibility of coupling pre-processing to CNN in order to improve their feature extraction capability. The combination of such different descriptors/methods in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches based on feature selection and classification. The experimental evaluation on three large publicly available datasets demonstrates high classification accuracy and f-measure of our ensemble with respect to other classifiers on the same datasets. One of the main contributions of this work is a collection of classification models and a wide experimental evaluation to report performance of both single CNN and ensemble of CNNs in different available plankton datasets with a given testing protocol. Moreover, we show how to combine different CNN in order to improve the performance. To encourage future comparisons the MATLAB source code is available in the GitHub repository: https://github.com/LorisNanni