Unsupervised learning of discriminative relative visual attributes

Abstract

Unsupervised learning of relative visual attributes is important because it is often infeasible for a human annotator to predefine and manually label all the relative attributes in large datasets. We propose a method for learning relative visual attributes given a set of images for each training class. The method is unsupervised in the sense that it does not require a set of predefined attributes. We formulate the learning as a mixed-integer programming problem and propose an efficient algorithm to solve it approximately. Experiments show that the learned attributes can provide good generalization and tend to be more discriminative than hand-labeled relative attributes. While in the unsupervised setting the learned attributes do not have explicit names, many are highly correlated with human annotated attributes and this demonstrates that our method is able to discover relative attributes automatically. © 2012 Springer-Verlag.