Extensible image object co-segmentation with sparse cooperative relations

Abstract

In the co-segmentation problems of sparse related image group, how to mine correlations between common objects has been one of the most significant steps. In this paper, we first prioritize the processing efficiency and construct sparse cooperative graph for related images in feature space, instead of examining the full correlations of common foregrounds which lead to high computation cost and great risk of false connection. And more importantly, we design a flexible and efficient extending strategy to reuse the cooperative graph for incremental image data. Then, with the sparse correlations of the image group, we propose a unilateral proposal co-selection method for pair-wise region level co-segmentation. Finally, we have achieved the segmentation results with further pixel level refinement. Our experimental results on publicly available datasets show that, compared with the approaches using dense cooperative constraints, the proposed method can achieve more competitive results with extremely sparse correlative constraints, which shows its bright application prospects for sparse correlated image groups with incremental data due to its high efficiency and flexible extensibility.