New set of generalized legendre moment invariants for pattern recognition

Abstract

In this paper, we present a new set of rotation, scale and translation invariants, named Generalized Legendre Moment Invariants (GLMI). This new set of invariants is defined on the Cartesian coordinate system, where we can derive the GLMI based on the algebraic relation between the fractional-order Legendre polynomials and the geometric basis. Consequently, several experiments are carried out to evaluate the performance of the proposed GLMI, with regard to their invariability property, object recognition capability and computation efficiency, in comparison with the most representative families of moment invariants. In addition, we have presented a systematic parameter selection method for finding the optimal fractional parameter values with respect to pattern recognition applications. Just as important, we have introduced an adaptive scheme to set the fractional parameters according to the characteristics of the image. The obtained results clearly show that the proposed invariants provide higher features accuracy and discrimination power even in the presence of noisy effects.