Automatic plankton image recognition with co-occurrence matrices and Support Vector Machine

Abstract

A long-standing problem in plankton ecology is sparseness of taxa-specific data. New optical imaging systems are becoming available which can acquire high-resolution data on the abundance and biomass of plankton taxa. The Video Plankton Recorder (VPR) has been designed and used for automatic sampling and visualization of major planktonic taxa at sea in real time, providing high-resolution data over a broad range of scales. Although these optical systems produce digital images of plankton that can be automatically identified by computer, the limited accuracy of automatic classification methods can reduce confidence in subsequent abundance estimates, especially in areas where a taxon is in low relative abundance. This paper describes an improved classification system for automatic identification of plankton taxa from digital images. Classifiers are trained from a set of images that were classified by human experts. The data set used to verify the classification system contains over 20 000 planktonic images manually sorted into 7 different categories. The new method uses co-occurrence matrices (COM) as the feature, and a Support Vector Machine (SVM) as the classifier. This new method is compared against a previous plankton recognition system, which used moment invariants, Fourier descriptors and granulometry as features and a learning vector quantization neural network as a classifier. The new method reduced the classification error rate from 39 to 28%. Subsequent plankton abundance estimates are improved by more than 50% in regions of low relative abundance. In general, the reduction in classification error was due to a combination of the use of COM and SVM.