  Learning with complete or partial supervision is powerful but relies on
ever-growing human annotation efforts. As a way to mitigate this serious
problem, as well as to serve specific applications, unsupervised learning has
emerged as an important field of research. In computer vision, unsupervised
learning comes in various guises. We focus here on the unsupervised discovery
and matching of object categories among images in a collection, following the
work of Cho et al. 2015. We show that the original approach can be reformulated
and solved as a proper optimization problem. Experiments on several benchmarks
establish the merit of our approach.

Computer Vision and Pattern Recognition