Real-Time Red Tide Algae Classification Using Naive Bayes Classifier and SVM

This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems. The difficulties of such classification includes: (1) the shape of the same algae category is deformable, and largely variant due to the individual differences and mature stage; (2) the image of algae may vary due to different 3D positions to the imaging plane and partial occlusion; (3) the images also contain unknown algae and contaminations. In the proposed method, several shape features were developed, a naive Bayes classifier was trained to reject the contaminative objects and unknown algae and a support vector machine (SVM) was used to classify the algae to taxonomic categories. Our approach achieved greater 90% accuracy on a collection of algal images. The test on contaminated algal image set (contains unknown algae and non-algae objects, such as sands) also demonstrated promising results.