Abstract
Shape models provide a compact parameterization of a
class of shapes, and have been shown to be important to
a variety of vision problems, including object detection,
tracking, and image segmentation. Learning generative
shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with
intractable marginals and posteriors, (2) high-dimensional
shape spaces with limited training samples (and the associated risk of overfitting), and (3) estimation of hyperparameters relating to model complexity that often entails computationally expensive grid searches. In this paper, we propose a Bayesian treatment that relies on direct probabilistic formulation for learning generative shape models in the
silhouettes space. We propose a variational approach for
learning a latent variable model in which we make use of,
and extend, recent works on variational bounds of logisticGaussian integrals to circumvent intractable marginals and
posteriors. Spatial coherency and sparsity priors are also
incorporated to lend stability to the optimization problem
by regularizing the solution space while avoiding overfitting
in this high-dimensional, low-sample-size scenario. We deploy a type-II maximum likelihood estimate of the model hyperparameters to avoid grid searches. We demonstrate that
the proposed model generates realistic samples, generalizes
to unseen examples, and is able to handle missing regions
and/or background clutter, while comparing favorably with
recent, neural-network-based approaches