Region Proposal Ranking via Fusion Feature for Object Detection

Region proposals followed by a classification network is a famous structure in object detection task. Recently, there are a lot of proposal methods that work well via enough samplings. However, how to use as few samples as possible to achieve high recall performance is still a difficult problem. In this paper, we propose a novel region proposal method via fusion features for general object detection. By establishing an encoding of object attributes, an objectness candidate inference method based on energy function is proposed for region proposal ranking, followed by a multi-level non maximum suppression strategy for further adjusting. Experiment on PASCAL VOC shows that our method achieves better performance on region proposal and object detection tasks.