Pattern hashing - object recognition based on a distributed local appearance model

This paper proposes "Pattern Hashing" as a new scheme for object recognition by effectively introducing an appearance-based approach into the framework of a geometric feature-based approach. We compose multiple bases using a combination of arbitrary three interest points in the model object, compute the geometric invariant for similarity transformation for each basis, and apply a hash function to it. Each image patch consists of pixels which are near the basis vector. We divide the model object image into multiple partial image patches, and create various appearances on the hash table as a distributed local appearance model. In the recognition stage, fast model selection is efficiently executed by the hashing technique, and then appearance pattern matching and voting procedure extract the target object in the input image. Through experiment with a face image database, we demonstrate that partly occluded object regions or multiple object positions can indeed be detected by the proposed algorithm.