Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals

TL;DR

This paper proposes an unsupervised, part-based region matching method for discovering and localizing dominant objects across diverse images without annotations, outperforming existing techniques on standard benchmarks.

Contribution

It introduces a novel unsupervised approach using probabilistic Hough matching of region proposals for object discovery and localization without any supervision.

Findings

Outperforms state-of-the-art in colocalization tasks

Robustly discovers objects in mixed-class datasets

Effective without image-level annotations or single dominant class assumption

Abstract

This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully unsupervised, without even image-level annotations or any assumption of a single dominant class. This is far more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based region 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 for each candidate correspondence considering both appearance 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 robust object discovery in challenging mixed-class datasets.