# Co-segmentation for Space-Time Co-located Collections

## TL;DR

This paper introduces a weakly-supervised co-segmentation method for space-time co-located image collections, effectively identifying common foreground objects despite high variability and ambiguity.

## Contribution

It proposes a distributed, belief-propagation-based approach that leverages minimal supervision and the entire image set without global modeling, improving co-segmentation accuracy.

## Key findings

- Outperforms previous methods on challenging datasets
- Handles high variability in object appearance
- Effective with minimal supervision

## Abstract

We present a co-segmentation technique for space-time co-located image collections. These prevalent collections capture various dynamic events, usually by multiple photographers, and may contain multiple co-occurring objects which are not necessarily part of the intended foreground object, resulting in ambiguities for traditional co-segmentation techniques. Thus, to disambiguate what the common foreground object is, we introduce a weakly-supervised technique, where we assume only a small seed, given in the form of a single segmented image. We take a distributed approach, where local belief models are propagated and reinforced with similar images. Our technique progressively expands the foreground and background belief models across the entire collection. The technique exploits the power of the entire set of image without building a global model, and thus successfully overcomes large variability in appearance of the common foreground object. We demonstrate that our method outperforms previous co-segmentation techniques on challenging space-time co-located collections, including dense benchmark datasets which were adapted for our novel problem setting.