Learning arbitrary-shape object detector from bounding-box annotation by searching region-graph

摘要

Arbitrary-shape is argued more precise than bounding-box for object detection. However, an arbitrary shape detector usually requires pixel-level human annotation, which is very expensive and hardly afforded for any real-world application. On the other hand, bounding-box is much easier than pixel-wise segmentation in human labeling. In this paper we aim to realize the arbitrary-shape detection from bounding-box human annotation. To this end, we propose location positiveness, which encodes the information of bounding-box annotation to help obtain region annotation. In addition, we propose two graph-based methods to embed the location positiveness, which enable more accurate model trained from simpler annotation. Experimental results validate the performance of our method. (C) 2016 Elsevier B.V. All rights reserved.