Abstract

Many computer vision methods rely on annotated image sets without taking advantage of the increasing number of unlabeled images available. This paper explores an alternative approach involving unsu- pervised structure discovery and semi-supervised learning (SSL) in im- age collections. Focusing on ob ject classes, the first part of the paper contributes with an extensive evaluation of state-of-the-art image repre- sentations. Thus, it underlines the decisive influence of the local neigh- borhood structure and its direct consequences on SSL results and the importance of developing powerful ob ject representations. In a second part, we propose and explore promising directions to improve results by looking at the local topology between images and feature combination strategies.