Recognizing similarity through a constrained non-rigid transform

The recognition of the class or category of an object based on shape similarity, is an important problem in image databases. Categorizing objects not only helps in efficient image database organization for faster indexing but also allows shape similarity-based querying. The recognition of category is, however, a difficult problem since member objects of a class can show considerable variation in the size and position of individual features even when the overall shape similarity is maintained. In this paper we present an approach to recognizing the class or category of an object in the case where the similarity between member objects is specified by a constrained non-rigid transform. The class is characterized by a single model or prototype consisting of a set of non-overlapping regions and a set of motion (direction and extent) constraints that capture the relation between members of the class. The recognition of category is done by using region correspondence between model and image and recovering the constrained non-rigid transform corresponding to a member of the class that is nearest in shape to the image