Classification of similar 2-D objects by Wavelet-Sparse-Matrix (WSM) method

This paper proposes a novel method called Wavelet-Sparse-Matrix (WSM) to ex tract the spatial features of 2-D objects for classifying objects that have subtle differences. The differences between these objects are present in t he spatial orientations of the objects, or in the local positions of points on the contours of the objects. The separable wavelets are able to disting uish these differences and to separate them into three sparse subpatterns. Sparse matrix technique has the ability to rearrange nonzero elements in a sparse matrix by moving them as close together as possible. WSM method is a combination of these two techniques which can considerably improve the dis tinction of slightly dissimilar objects. Experiments are conducted, which i nclude a series of discriminative simulations and comparisons with Fourier descriptor and Zernike moment invariant. These experiments verify the feasi bility and effectiveness of the WSM method.