Researchers have long used topic modeling to automatically characterize and summarize text documents without supervision. Can we extract similar structures from collections of images? To do this, we propose visual vocabularies, a method to analyze image datasets by decomposing images into segments, and grouping similar segments into visual "words". These vocabularies of visual "words" enable us to extract visual topics that capture hidden themes distinct from what is captured by classic unsupervised approaches. We evaluate our visual topics using standard topic modeling metrics and confirm the coherency of our visual topics via a human study.