A two-stage-classifier for defect classification in optical media inspection

In this paper we address the problem of inspecting optical media like compact disks and digital versatile disks. Here, defective disks have to be identified during production. For optimizing the production process and in order to be able to decide how critical a certain defect is, the defects found have to be classified. As this has to be done online, the classification algorithm has to work very fast. With regard to speed, the well known minimum distance classifier is usually a good choice. However, when training data are not well clustered in the feature-space this classifier becomes rather unreliable. To trade-off speed and reliability we propose a two-stage-algorithm. It combines the fast minimum distance classification with a reliable fuzzy k-nearest neighbor classifier. The resulting two-stage-classifier is considerably faster than the fuzzy k-nearest neighbor classifier. Its classification rates are in the range of the fuzzy k-nearest neighbor classifier and far better than those of the minimum distance classifier. To evaluate the results, we compare them to the results obtained using various standard classifiers.