Efficient semantic segmentation with Gaussian processes and histogram intersection kernels

Semantic interpretation and understanding of images is an important goal of visual recognition research and offers a large variety of possible applications. One step towards this goal is semantic segmentation, which aims for automatic labeling of image regions and pixels with category names. Since usual images contain several millions of pixel, the use of kernel-based methods for the task of semantic segmentation is limited due to the involved computation times. In this paper, we overcome this drawback by exploiting efficient kernel calculations using the histogram intersection kernel for fast and exact Gaussian process classification. Our results show that non-parametric Bayesian methods can be utilized for semantic segmentation without sparse approximation techniques. Furthermore, in experiments, we show a significant benefit in terms of classification accuracy compared to state-of-the-art methods.