This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection of multiple object classes. The setting of this problem is fully unsupervised, without evn image-level annotations or any assumption of a single dominant class. This is significantly more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based matching approach: We use off-the-shelf region proposals to form a set of candidate bounding boxes for objects and object parts. These regions are efficiently matched across images using a probabilistic Hough transform that evaluates the confidence in each candidate region considering both appearance similarity and spatial consistency. Dominant objects are discovered and localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach significantly outperforms the current state of the art in colocalization, and achieves ro-bust object discovery in challenging mixed-class datasets.