Bagging based plankton image classification

Abstract

Plankton image classification plays an important role in ocean biological research. In this paper, we present an approach based on the bagging technique to classify the marine plankton images captured by the Shadowed Image Particle Profiling and Evaluation Recorder. The difficulty of such classification is multifold because the data set is much noisier, and the plankton images are deformable, projection-variant, and often in partial occlusion. In addition, the images in our experiments are binary, thus are lack of pixel-depth information. By random sampling with replacement on the original training set, a number of independent bootstrap replicates are generated. Using these replicates as new training sets, we construct multiple classifiers that are complementary of one another. While such individual classifiers are less effective than a single classifier trained on the whole training set, the fusion of them using majority voting produces an improved tenfold cross-validation accuracy by more than 93%.