The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals

Ahmad Humayun, Fuxin Li, James M. Rehg

Abstract: Object proposals have recently fueled the progress in detection performance. These proposals aim to provide category-agnostic localizations for all objects in an image. One way to generate proposals is to perform parametric min-cuts over seed locations. This paper demonstrates that standard parametric-cut models are ineffective in obtaining medium-sized objects, which we refer to as the middle child problem. We propose a new energy minimization framework incorporating geodesic distances between segments which solves this problem. In addition, we introduce a new superpixel merging algorithm which can generate a small set of seeds that reliably cover a large number of objects of all sizes. We call our method POISE - "Proposals for Objects from Improved Seeds and Energies." POISE enables parametric min-cuts to reach their full potential. On PASCAL VOC it generates ~2,640 segments with an average overlap of 0.81, whereas the closest competing methods require more than 4,200 proposals to reach the same accuracy. We show detailed quantitative comparisons against 5 state-of-the-art methods on PASCAL VOC and Microsoft COCO segmentation challenges.