Large scale Gaussian Process for overlap-based object proposal scoring

Abstract

This work considers the task of object proposal scoring by integrating the consistency between state- of-the-art object proposal algorithms. It represents a novel way of thinking about proposals, as it starts with the assumption that consistent proposals are most likely centered on objects in the image. We pose the box-consistency problem as a large-scale regression task. The approach starts from existing popular object proposal algorithms and assigns scores to these proposals based on the consistency within and be- tween algorithms. Rather than generating new proposals, we focus on the consistency of state-of-the-art ones and score them on the assumption that mutually agreeing proposals usually indicate the location of objects. This work performs large-scale regression by starting from the strong Gaussian Process model, renowned for its power as a regressor. We extend the model in a natural manner to make effective use of the large number of training samples. We achieve this through metric learning for reshaping the kernel space, while maintaining the kernel-matrix size fixed. We validated the new Gaussian Process models on a standard regression dataset —Airfoil Self-Noise —to prove the generality of the method. Further- more, we test the suitability of the proposed approach for the undertaken box scoring task on Pascal- VOC2007. We conclude that box scoring is possible by employing overlap statistics in a new Gaussian Process model, fine tuned to handle large amounts of data.