  What defines a visual style? Fashion styles emerge organically from how
people assemble outfits of clothing, making them difficult to pin down with a
computational model. Low-level visual similarity can be too specific to detect
stylistically similar images, while manually crafted style categories can be
too abstract to capture subtle style differences. We propose an unsupervised
approach to learn a style-coherent representation. Our method leverages
probabilistic polylingual topic models based on visual attributes to discover a
set of latent style factors. Given a collection of unlabeled fashion images,
our approach mines for the latent styles, then summarizes outfits by how they
mix those styles. Our approach can organize galleries of outfits by style
without requiring any style labels. Experiments on over 100K images demonstrate
its promise for retrieving, mixing, and summarizing fashion images by their
style.
