Feature Selection on Affine Moment Invariants in Relation to Known Dependencies

Moment invariants are one of the techniques of feature extraction frequently used for pattern recognition algorithms. A moment is a projection of function into polynomial basis and an invariant is a function returning the same value for an input with and without particular class of degradation. Several techniques of moment invariant creation exist often generating over-complete set of invariants. Dependencies in these sets are commonly in a form of complicated polynomials, furthermore they can contain dependencies of higher orders. These theoretical dependencies are valid in the continuous domain but it is well known that in discrete cases are often invalidated by discretization. Therefore, it would be feasible to begin classification with such an over-complete set and adaptively find the pseudo-independent set of invariants by the means of feature selection techniques. This study focuses on testing of the influence of theoretical invariant dependencies in discrete pattern recognition applications.