  Despite recent improvements using fully convolutional networks, in general,
the segmentation produced by most state-of-the-art semantic segmentation
methods does not show satisfactory adherence to the object boundaries. We
propose a method to refine the segmentation results generated by such deep
learning models. Our method takes as input the confidence scores generated by a
pixel-dense segmentation network and re-labels pixels with low confidence
levels. The re-labeling approach employs a region growing mechanism that
aggregates these pixels to neighboring areas with high confidence scores and
similar appearance. In order to correct the labels of pixels that were
incorrectly classified with high confidence level by the semantic segmentation
algorithm, we generate multiple region growing steps through a Monte Carlo
sampling of the seeds of the regions. Our method improves the accuracy of a
state-of-the-art fully convolutional semantic segmentation approach on the
publicly available COCO and PASCAL datasets, and it shows significantly better
results on selected sequences of the finely-annotated DAVIS dataset.
