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Successive pattern classification based on test feature classifier and its application to defect image classification
Authors:Yukinobu Sakata,Shuni&rsquo  chi Kaneko,Hirohito Okuda
Affiliation:a Graduate School of Engineering, Hokkaido University, N13-W8 Sapporo, Japan
b Graduate School of Information Science, Hokkaido University, N14-W9 Sapporo, Japan
c Hitachi Co. Ltd, Production Engineering Research Laboratory, Yokohama, Japan
Abstract:A novel successive learning algorithm based on a Test Feature Classifier is proposed for efficient handling of sequentially provided training data. The fundamental characteristics of the successive learning are considered. In the learning, after recognition of a set of unknown data by a classifier, they are fed into the classifier in order to obtain a modified performance. An efficient algorithm is proposed for the incremental definition of prime tests which are irreducible combinations of features and capable of classifying training patterns into correct classes. Four strategies for addition of training patterns are investigated with respect to their precision and performance using real pattern data. A real-world problem of classification of defects on wafer images has been dealt with by the proposed classifier, obtaining excellent performance even through efficient addition strategies.
Keywords:Classification   Test feature classifier   Successive learning   Defect image
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