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Hierarchical approach to feature indexing

We extend our previous approach to 2-D shape indexing, data-driven indexed hypotheses, to hierarchical features. It is shown mathematically and experimentally that an index based on hierarchical features is more computationally efficient than one based on nonhierarchical features. Our approach addresses two types of hierarchies: a multilevel approximation of the contours of 2-D objects and a three-level feature indexing system. Our approach can easily be extended to 3-D objects. As we know, occluded object recognition should be based on local features. However, these features may sometimes be lost due to changes of scale as well as to occlusion. Our first hierarchical mechanism is used to complement the feature loss due to scale changes. It results in multilevel approximations of the contours of objects using scale-space approaches. This approach is also beneficial when there are few boundary points of maximal curvature so that standard polygonal approximation schemes don''t work very well. Our second hierarchical mechanism is to use sets of visible local features to hypothesize the presence of objects. Verification of the various hypotheses is done via normalization and boundary template matching. Using these two hierarchical mechanisms results in an approach to shape recognition which is very general in practice. Theoretically, many types of features can be recognized, though line features are preferred. Our technique offers a new approach to the construction of indexing systems for image databases on the basis of image features.