Nonparametric Learning for Layered Segmentation of Natural Images
We explore recently proposed Bayesian nonparametric models of image partitions, based on spatially dependent Pitman-Yor processes. These models are attractive because they adapt to images of varying complexity, successfully modeling uncertainty in the structure and scale of human segmentations of natural scenes. By developing substantially improved inference and learning algorithms, we achieve performance comparable to state-of-the-art methods. For learning, we show how the Gaussian process (GP) covariance functions underlying these models can be calibrated to accurately match the statistics of example human segmentations. For inference, we develop a stochastic search-based algorithm which is substantially less susceptible to local optima than conventional variational methods. Our approach utilizes the expectation propagation algorithm to approximately marginalize latent GPs, and a low rank covariance representation to improve computational efficiency. Experiments with two benchmark datasets show that our learning and inference innovations substantially improve segmentation accuracy. By hypothesizing multiple partitions for each image, we also take steps towards capturing the variability of human scene interpretations.