In recent years, databases have evolved from storing pure textual information to storing multimedia information - text, audio, video, and images. With such databases comes the need for a richer set of search keys that include keywords, shapes, sounds, examples, sketches, color, texture and motion. In this paper we address the problem of image retrieval where keys are object shapes or user sketches. In our scheme, shape features are extracted from each image as it is stored. The image is first segmented and points of high curvature are extracted. Regions surrounding the points of high curvature are used to compute feature values by comparing the regions with a number of references. The references themselves are picked out from the set of orthonormal wavelet basis vectors. An ordered set of distance measures between each local region and the wavelet references form a feature vector. When a user queries the database through a sketch, the feature vectors for high curvature points on the sketch are determined. An efficient nearest neighbor search then yields a set of images which contain objects that match the user's sketch closely. The process is completely automated. Initial experimental results are presented.
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