Background Context Augmented Hypothesis Graph for Object Segmentation

In this paper we address the problem of semantic segmentation. Inspired by the significant role of the context information, in this task our solution makes use of semantically meaningful overlapping object hypotheses augmented by contextual information, which is obtained from a novel background mining procedure. More precisely, a fully connected conditional random field is considered over a set of overlapping segment hypotheses and the unlabeled background regions are learned from a training set and applied in the unary terms corresponding to the foreground regions. The final segmentation result is obtained via maximum a posteriori inference, in which the segments are merged based on a sequential aggregation followed by morphological hole filling and superpixel refinement serving as postprocessing. Moreover, by incorporating other kinds of contextual cues, such as global image classification and object detection cues, a new state-of-the-art performance is achieved by our proposed solution as experimentally verified on the challenging PASCAL Visual Object Class (VOC) Challenge 2012 and MSRC-21 object segmentation data sets.