Binary SIPPER plankton image classification using random subspace

Abstract

Plankton recognition plays an important role in the ocean environmental research. In this paper, we propose a random subspace based algorithm to classify the plankton images detected in real time by the Shadowed Image Particle Profiling and Evaluation Recorder. The difficulty of such classification is multifold because the data sets are not only much noisier but the plankton are deformable, projection-variant, and often in partial occlusion. In addition, the images in our experiments are binary, thus are lack of texture information. Using random sampling, we construct a set of stable classifiers to take full advantage of nearly all the discriminative information in the feature space of plankton images. The combination of multiple stable classifiers is better than a single classifier. We achieve over 93% classification accuracy on a collection of more than 3000 images, making it comparable with what a trained biologist can achieve by using conventional manual techniques.