Robustness of Shape Similarity Retrieval under Affine Transformation

The application of Curvature Scale Space representation in shape similarity retrieval under affine transformation is addressed in this paper. The maxima of Curvature Scale Space (CSS) image have already been used to represent 2-D shapes in different applications. The representation has shown robustness under the similarity transformations. Scaling, orientation changes, translation and even noise can be easily handled by the representation and its associated matching algorithm. In this paper, we also consider shear and examine the performance of the representation under affine transformations. It is observed that the performance of the method is promising even under severe deformations caused by shear. The method is tested on a very large database of shapes and also evaluated objectively through a classified database. The performance of the method is compared with the performance of two well-known methods, namely Fourier descriptors and moment invariants. We also observe that global parameters such as eccentricity and circularity are no longer useful in an affine transform environment.