What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach

We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images.