  Image co-segmentation has attracted a lot of attentions in computer vision
community. In this paper, we propose a new approach to image co-segmentation
through introducing the dense connections into the decoder path of Siamese
U-net and presenting a new edge enhanced 3D IOU loss measured over distance
maps. Considering the rigorous mapping between the signed normalized distance
map (SNDM) and the binary segmentation mask, we estimate the SNDMs directly
from original images and use them to determine the segmentation results. We
apply the Siamese U-net for solving this problem and improve its effectiveness
by densely connecting each layer with subsequent layers in the decoder path.
Furthermore, a new learning loss is designed to measure the 3D intersection
over union (IOU) between the generated SNDMs and the labeled SNDMs. The
experimental results on commonly used datasets for image co-segmentation
demonstrate the effectiveness of our presented dense structure and edge
enhanced 3D IOU loss of SNDM. To our best knowledge, they lead to the
state-of-the-art performance on the Internet and iCoseg datasets.
