Implicit shape kernel for discriminative learning of the hough transform detector

Abstract

The Hough transform provides an efficient way to detect objects. Various methods have been proposed to achieve discriminative learning of the Hough transform, but they have usually focused on learning discriminative weights to the local features, which reflect whether the features are matched on the object or the background. In this paper, we propose a novel approach to put the whole Hough transform into a maximum margin framework, including both the weights for the local features and their locations. This is achieved through the kernel methods of the SVM. We propose a kernel that can be used to learn a SVM classifier that determines the presence of the object in a subimage. The kernel is designed such that during testing, the standard Hough transform process can be used to obtain the exact decision scores of the SVM at every location and scale of a test image. The experiment results show that our approach significantly improves the detection performance over previous methods of learning the Hough transform.