In this paper, we implement 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 compounded 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.
Binary plankton image classification using random subspace
01.01.2005
137426 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Binary Plankton Image Classification using Random Subspace
British Library Conference Proceedings | 2005
|Subspace analysis using random mixture models
IEEE | 2005
|NTRS | 1966
|Multiple Similarities Based Kernel Subspace Learning for Image Classification
British Library Conference Proceedings | 2006
|Random Sampling for Subspace Face Recognition
British Library Online Contents | 2006
|