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.

图像共分割的重要意义在于它可以通过探索相关图像之间的相关性来减轻图像分割的不适性。在过去的十年中，已经开发了许多自动图像共分割算法，本文对此进行了全面的研究。我们首先分析视觉/语义线索以指导图像的共同细分，包括对象线索和相关线索。然后，我们在基于对象元素，基于对象区域/轮廓，基于常见对象模型的三类中描述了传统方法。在下一部分中，将回顾基于深度学习的方法。此外，介绍了广泛使用的测试数据集和评估标准，并将所调查算法的报告性能进行了比较。最后，我们讨论了当前的挑战和可能的未来方向，并总结了本文。希望这项全面的研究将有助于图像共分割技术的发展。