Application of Statistical Learning Theory to Plankton Image Analysis
MASSACHUSETTS INST OF TECH CAMBRIDGE JOINT PROGRAM IN APPLIED OCEAN SCIENCE AND ENGINEERING
Pagination or Media Count:
A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder VPR. The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components pattern representationfeature measurement, feature extractionselection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization LVQ neural network NN classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale co-occurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12 in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed. One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier NN and SVM voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not good ie, representative of the field data.
- Physical and Dynamic Oceanography
- Geology, Geochemistry and Mineralogy