In this paper, we present a set of wavelet moment invariants, together with
a discriminative feature selection method, for the classification of seemi
ngly 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 momen
t invariants achieved the highest classification rate for all four differen
t sets tested, compared with Zernike's moment invariants and Li's moment in
variants. For a test set consisting of 26 upper cased English letters, wave
let moment invariants could obtain 100% classification rate when applied to
26 x 30 randomly generated noisy and scaled letters, whereas Zernike's mom
ent invariants and Li's moment invariants obtained only 98.7 and 75.3%, res
pectively. The theoretical and experimental analyses in this paper prove th
at the proposed method has the ability to classify many types of image obje
cts, and is particularly suitable for classifying seemingly similar objects
with subtle differences. (C) 1999 Pattern Recognition Society. Published b
y Elsevier Science Ltd. All rights reserved.