Elastic Edge Boxes for Object Proposal on RGB-D Images

Object proposal is utilized as a fundamental preprocessing of various multimedia applications by detecting the candidate regions of objects in images. In this paper, we propose a novel object proposal method, named elastic edge boxes, integrating window scoring and grouping strategies and utilizing both color and depth cues in RGBD images. We first efficiently generate the initial bounding boxes by edge boxes, and then adjust them by grouping the super-pixels within elastic range. In bounding boxes adjustment, the effectiveness of depth cue is explored as well as color cue to handle complex scenes and provide accurate box boundaries. To validate the performance, we construct a new RGB-D image dataset for object proposal with the largest size and balanced object number distribution. The experimental results show that our method can effectively and efficiently generate the bounding boxes with accurate locations and it outperforms the state-of-the-art methods considering both accuracy and efficiency.