Structural Shape Indexing with Feature Generation Models

Structural indexing is a potential approach to efficient classification and retrieval of image patterns with respect to a very large number of models. This technique is based on the idea of distributing features associated with model identifiers over a large data structure prepared for a model set, along with classification by voting for models with reference to the extracted features. Essential problems caused by mapping image features to discrete indices are that indexing is sensitive to noise, scales of observation, and local shape deformations, and thata prioriknowledge and 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. An analysis of feature transformations is carried out for some particular types of shape deformations, leading to feature generation rules composed of a small number of distinct cases. The rules are exploited to generate features that can be extracted from deformed patterns caused by noise and local shape deformations. In both processes of model database organization and classification, the generated features by the transformation rules are used for structural indexing and voting, as well as the features actually extracted from contours. The effectiveness of the proposed method is demonstrated by experimental trials with a large number of sample data. Furthermore, its application to shape retrieval from image databases is mentioned. The shape feature generation significantly improves the classification accuracy and efficiency.