Collection Statistical Modeling for Shapes and Imaging
Organizer(s)
Date(s)
03/05/2024
Maximum Entropy Models for Texture Synthesis
The problem of examplar-based texture synthesis consists in producing an image that has the same perceptual aspect as a given texture sample. It can be formulated as sampling an image which is 'as random as possible' while satisfying some constraints that are linked to the textural aspect. Many solutions have been proposed, often lying between stochastic models and variational methods. In this talk, we will present a solution that relies on the sampling of a maximum entropy distribution. The parameters of the model are fixed in order to preserve (in expectation) the values of a feature transform (which encodes the textural aspect). The estimation of these parameters from a single original texture relies on a stochastic optimization procedure. Sampling the model relies on a MCMC procedure and we will detail several examples of features for which we can use a provably-convergent Langevin sampling algorithm. In particular, we will show that sampling a maximum entropy model based on a smooth convolutional neural network allows to produce plausible texture samples with a relatively small set of parameters. We will also give some insights on the link with the simpler method based on gradient descent starting from a random initialization. This is a joint work with Valentin de Bortoli, Agnès Desolneux, Alain Durmus and Bruno Galerne.
