Maplets for correspondence-based object recognition

We present a correspondence-based system for visual object recognition with invariance to position, orientation, scale and deformation. The system is intermediate between high- and low-dimensional representations of correspondences. The essence of the approach is based on higher-order links, called here maplets, which are specific to narrow ranges of mapping parameters (position, scale and orientation), which interact cooperatively with each other, and which are assumed to be formed by learning. While being based on dynamic links, the system overcomes previous problems with that formulation in terms of speed of convergence and range of allowed variation. We perform face recognition experiments, comparing ours to other published systems. We see our work as a step towards a reformulation of neural dynamics that includes rapid network self-organization as essential aspect of brain state organization.