Real-time red tide algae recognition using SVM and SVDD

This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems. The classifier was required to reject non-object (non-target) algae and contaminative objects, which are not seen during training, thus producing a very difficult problem. In the proposed method, a support vector data description (SVDD), which offers good rejection ability, 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.