An Affine Invariant Function using PCA Bases with an Application to Within-Class Object Recognition

The problem of shape-based recognition of objects under affine transformations is considered. We focus on the construction of a robust and highly discriminative affine invariant function that can be used for within-class object recognition applications. Using the boundaries of the objects of interest, a training scheme, based on principal component analysis (PCA), is proposed to derive a set of basis functions with desired properties. The derived bases are then used for the construction of a novel affine invariant function. The proposed invariant function is evaluated for the problem of aircraft silhouette identification and appears to achieve comparable performance to a popular wavelet-based affine invariant function. At the same time, the proposed framework is much simpler than that based on wavelet analysis.