An Efficient Shape-Based Approach to Image Retrieval

We consider the problem of finding the best match for a given query shape among candidate shapes stored in a shape base. This is central to a wide range of applications, such as, digital libraries, digital film databases, environmental sciences, and satellite image repositories. We present an efficient matching a gorithm built around a novel similarity criterion and based on shape normalization about the shape's diameter, which reduces the effects of noise or limited accuracy during the shape extraction procedure. Our matching a gorithm works by gradually "flattening" the query shape until the best match is discovered. The algorithm exhibits poly-logarithmic time behavior assuming uniform distribution of the shape vertices in the locus of their normalized positions