Title: Intelligent plankton image classification with deep learning

Authors: Hussein Al-Barazanchi; Abhishek Verma; Shawn X. Wang

Addresses: Department of Computer Science, California State University, Fullerton, CA 92831, USA ' Department of Computer Science, New Jersey City University, Jersey City, NJ 07305, USA ' Department of Computer Science, California State University, Fullerton, CA 92831, USA

Abstract: Planktons are extremely diverse groups of organisms that exist in large water columns. They are sources of food for fishes and many other marine life animals. The plankton distribution is essential for the survival of many ocean lives and plays a critical role in marine ecosystem. In recent years, intelligent image classification systems were developed to study plankton distribution through classification of the plankton images taken by underwater imaging devices. Due to the significant differences in both shapes and sizes of the plankton population, accurate classification poses a daunting challenge. The mixed quality of the collected images adds more difficulty to the task. In this paper, we present an intelligent machine learning system built on convolutional neural networks (CNN) for plankton image classification. Unlike most of the existing image classification algorithms, CNN based systems do not depend on features engineering and they can be efficiently extended to encompass new classes. The experimental results on SIPPER image datasets show that the proposed system achieves higher accuracy compared with the state-of-the-art approaches. The new system is also capable of learning a much larger number of plankton classes.

Keywords: shadowed image particle profiling and evaluation recorder; SIPPER plankton image; convolutional neural network; CNN; deep learning; image classification.

DOI: 10.1504/IJCVR.2018.095584

International Journal of Computational Vision and Robotics, 2018 Vol.8 No.6, pp.561 - 571

Received: 30 Dec 2016
Accepted: 06 Sep 2017

Published online: 11 Oct 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article