This paper addresses semantic segmentation by using an augmented hypothesis graph with contextual information. It mines background information from a training set and applies it to the unary terms of foreground regions in a fully connected conditional random field model over overlapping segment hypotheses. The final segmentation is obtained through maximum a posteriori inference followed by post-processing steps. Experimental results on PASCAL VOC 2012 and MSRC-21 data sets show the approach achieves state-of-the-art performance by incorporating contextual cues such as image classification and object detection.

1. BACKGROUND CONTEXT AUGMENTED HYPOTHESIS GRAPH FOR OBJECT
SEGMENTATION
ABSTRACT
In this paper we address the problem of semanticsegmentation. Inspired by the significant
role of the contextinformation, in this task our solution makes use of semanticallymeaningful
overlapping object hypotheses augmented by contextualinformation, which is obtained from a
novel backgroundmining procedure. More precisely, a fully connected conditionalrandom field is
considered over a set of overlapping segmenthypotheses and the unlabeled background regions
are learnedfrom a training set and applied in the unary terms correspondingto the foreground
regions. The final segmentation result isobtained via maximum a posteriori inference, in which
thesegments are merged based on a sequential aggregation followedby morphological hole filling
and superpixel refinement servingas post processing. Moreover, by incorporating other kinds
ofcontextual cues, such as global image classification and objectdetection cues, a new state-of-
the-art performance is achievedby our proposed solution as experimentally verified on
thechallenging PASCAL Visual Object Class (VOC) challenge 2012and MSRC-21 object
segmentation data sets.