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Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Learning a Max-Entropy Diffusion Model for Visual Textures

Xinyuan Zhao¹, Eero P Simoncelli²; ¹New York University, ²Flatiron Institute

Presenter: Xinyuan Zhao

Visual textures, defined as spatially homogeneous image regions containing repeated elements (e.g. a field of grass or the bark of a tree), are prevalent in visual scenes and provide important cues for recognizing materials and objects. Existing texture models extract essential features from a single texture image, and can then generate high-quality samples that are visually similar to the original. However, their features are either hand-designed or based on a network pretrained for another purpose (e.g., object recognition). We develop a novel principled method for unsupervised learning of a set of statistics that are used to constrain a maximum entropy density model for the texture. We use training and sampling procedures derived from generative diffusion models. Our trained model is more compact (512 features), but generates texture images whose quality is as good as, and often better than, previous methods. We also demonstrate qualitative convexity of the representation space by generating samples that interpolate between two given textures.

Topic Area: Computational Models of Vision & Visual Cortex