The revised Fundamental Theorem of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Plankton Image Recognition
Artificial Intelligence Review
Spatial Size Distributions: Applications to Shape and Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Random sampling based SVM for relevance feedback image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Plankton recognition plays an important role in the ocean environmental research. In this paper, we propose a random subspace based algorithm to classify the plankton images detected in real time by the Shadowed Image Particle Profiling and Evaluation Recorder. The difficulty of such classification is multifold because the data sets are not only much noisier but the plankton are deformable, projection-variant, and often in partial occlusion. In addition, the images in our experiments are binary, thus are lack of texture information. Using random sampling, we construct a set of stable classifiers to take full advantage of nearly all the discriminative information in the feature space of plankton images. The combination of multiple stable classifiers is better than a single classifier. We achieve over 93% classification accuracy on a collection of more than 3000 images, making it comparable with what a trained biologist can achieve by using conventional manual techniques.