Associative memory of connectivity patterns

摘要

The goal of the visual correspondence problem is to establish a connectivity pattern (a mapping) between two images such that features projected from the same scene point are connected. Dynamic link matching (DLM) is a self-organizing dynamical system to establish such connectivity patterns for object recognition, but with rather naturally given simple interactions between pattern elements, its organizing process is slow. Here we propose to stabilize (store) established mappings so that they can be recovered efficiently and reliably in the future. This is implemented by modifying the underlying system of interactions using the established mappings as learning examples, where the Hebbian rule makes the adapted interactions proportional to the weights of an associative memory of these mappings. It is shown in simulation that the adapted interactions lead to faster and more robust DLM.