  Image co-segmentation is important for its advantage of alleviating the
ill-pose nature of image segmentation through exploring the correlation between
related images. Many automatic image co-segmentation algorithms have been
developed in the last decade, which are investigated comprehensively in this
paper. We firstly analyze visual/semantic cues for guiding image
co-segmentation, including object cues and correlation cues. Then we describe
the traditional methods in three categories of object elements based, object
regions/contours based, common object model based. In the next part, deep
learning based methods are reviewed. Furthermore, widely used test datasets and
evaluation criteria are introduced and the reported performances of the
surveyed algorithms are compared with each other. Finally, we discuss the
current challenges and possible future directions and conclude the paper.
Hopefully, this comprehensive investigation will be helpful for the development
of image co-segmentation technique.
