Unsupervised Cosegmentation based on Global Graph Matching

Abstract

Cosegmentation is defined as the task of segmenting a common object from multiple images. Hitherto, graph matching has been known as a promising approach because of its flexibility in matching deformable objects and regions, and several methods based on this approach have been proposed. However, candidate foregrounds obtained by a local matching algorithm in previous methods tend to include false-positive areas, particularly when visually similar backgrounds (e.g., sky) commonly appear across images. We propose an unsupervised cosegmentation method based on a global graph matching algorithm. Rather than using a local matching algorithm that finds a small common subgraph, we employ global matching that can find a one-to-one mapping for every vertex between input graphs such that we can remove negative regions estimated as background. Experimental results obtained using the iCoseg and MSRC datasets demonstrate that the accuracy of the proposed method is higher than that of previous graph-based methods.