Abstract
We present a system to recognize underwater plankton images from the Shadow Image Particle Profiling Evaluation Recorder. As some images do not have clear contours, we developed several features that do not heavily depend on the contour information. A soft margin support vector machine (SVM) was used as the classifier. We developed a new way to assign probability after multi-class SVM classification. Our approach achieved approximately 90% accuracy on a collection of images with minimal noise. On another image set containing manually unidentifiable particles, it also provided promising results. Furthermore, our approach is more accurate on the two data sets than a C4.5 decision tree and a cascade correlation neural network at the 95% confidence level.
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