Marine Phytoplankton Recognition Using Hybrid Classification Methods

Marine phytoplanktons are unicellular algae with a variety of shapes and ornamentation, and they are widely used as indicators of marine ecosystem changes. A dual layer and hybrid classifier is presented in this study for phytoplankton recognition. The method is based on k-NN, SVM mechanisms and uses shape and texture information such as moments, geometric features and gray level co-occurrence matrix features. Each individual classifier has its own specific input feature and decision mechanism. The marine phytoplankton recognition experiment shows that the proposed classification method outperforms two well-known stand-alone classifiers, k-NN and SVM.