Curvature scale space image in shape similarity retrieval

Abstract

In many applications, the user of an image database system points to an image, and wishes to retrieve similar images from the database. Computer vision researchers aim to capture image information in feature vectors which describe shape, texture and color properties of the image. These vectors are indexed or compared to one another during query processing to find images from the database. This paper is concerned with the problem of shape similarity retrieval in image databases. Curvature scale space (CSS) image representation along with a small number of global parameters are used for this purpose. The CSS image consists of several arch-shape contours representing the inflection points of the shape as it is smoothed. The maxima of these contours are used to represent a shape. The method is then tested on a database of 1100 images of marine creatures. A classified subset of this database is used to evaluate the method and compare it with other methods. The results show the promising performance of the method and its superiority over Fourier descriptors and moment invariants.