Structural indexing of line pictures with feature generation models

Abstract

Structural indexing is a potential approach to efficient classification and retrieval of image patterns with respect to a very large number of models. Essential problems caused by mapping image features to discrete indices are that the indexing is sensitive to noise, scales of observation, and local shape deformations, and that a priori knowledge or feature distributions of corrupted instances are not available for each class when a large number of training data are not presented. To cope with these problems, shape feature generation techniques are incorporated into structural indexing. The feature transformation rules obtained by an analysis of some particular types of shape deformations are exploited to generate features that can be extracted from deformed patterns. The generated features are used in model database organization and classification. Experimental trials with a large number of sample data show that the shape feature generation significantly improves the classification accuracy and efficiency.