A Minimum Barrier Distance based Saliency Box for Object Proposals Generation

Abstract

Object proposals generation plays an important role in computer vision. A good object proposals generation model should assign obviously high and low objectness score to the window that contains complete objects and incomplete objects, respectively. However, some existing methods such as local contrast based models usually fail to satisfy this requirement. In this letter, we propose MBDSal Box, a minimum barrier distance (MBD) based saliency box for locating object proposals. MBDSal Box consists of three components: item: First, a window saliency computation module that calculates the MBD saliency of each sliding window; second, a window refinement module that provides more accurate bounding boxes by a marker based watershed algorithm; third, a window scoring module which combines multiple features to compute the final objectness score. The experimental results carried on PASCAL VOC 2007 and Microsoft COCO 2014 datasets show that our model achieves better performance than the state-of-the-art models with competitive speed.