Large scale Gaussian Process for overlap-based object proposal scoring

Combination of state-of-the-art object proposals.Gaussian Process regression for learning object-proposal goodness.Effective Gaussian Process regression through clustering and learning the kernel distances in a metric learning formalism.Object-proposal scoring based solely on overlap consistency between the proposals of different state-of-the-art object proposal algorithms. No appearance features are required by our method. Display Omitted 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 between 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. Furthermore, 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.