  Deep Metric Learning (DML) is helpful in computer vision tasks. In this
paper, we firstly introduce DML into image co-segmentation. We propose a novel
Triplet loss for Image Segmentation, called IS-Triplet loss for short, and
combine it with traditional image segmentation loss. Different from the general
DML task which learns the metric between pictures, we treat each pixel as a
sample, and use their embedded features in high-dimensional space to form
triples, then we tend to force the distance between pixels of different
categories greater than of the same category by optimizing IS-Triplet loss so
that the pixels from different categories are easier to be distinguished in the
high-dimensional feature space. We further present an efficient triple sampling
strategy to make a feasible computation of IS-Triplet loss. Finally, the
IS-Triplet loss is combined with 3 traditional image segmentation losses to
perform image segmentation. We apply the proposed approach to image
co-segmentation and test it on the SBCoseg dataset and the Internet dataset.
The experimental result shows that our approach can effectively improve the
discrimination of pixels' categories in high-dimensional space and thus help
traditional loss achieve better performance of image segmentation with fewer
training epochs.
