Discriminative wavelet shape descriptors for recognition of 2-D patterns

In this paper, we present a set of wavelet moment invariants, together with a discriminative feature selection method, for the classification of seemingly similar objects with subtle differences. These invariant features are selected automatically based on the discrimination measures defined for the invariant features. Using a minimum-distance classifier, our wavelet moment invariants achieved the highest classification rate for all four different sets tested, compared with Zernike's moment invariants and Li's moment invariants. For a test set consisting of 26 upper cased English letters, wavelet moment invariants could obtain 100% classification rate when applied to 26 × 30 randomly generated noisy and scaled letters, whereas Zernike's moment invariants and Li's moment invariants obtained only 98.7 and 75.3%, respectively. The theoretical and experimental analyses in this paper prove that the proposed method has the ability to classify many types of image objects, and is particularly suitable for classifying seemingly similar objects with subtle differences.