Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

• We provide a thorough overview of recent segmentation literature.
• Existing methods are classified, compared and discussed in the manuscript.
• We discuss the dataset and the metrics for evaluating the performance of existing methods.
• We include discussions for design choices and future research directions.

Image segmentation refers to the process to divide an image into meaningful non-overlapping regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. While many segmentation algorithms exist, there are only a few sparse and outdated summarizations available. Thus, in this paper, we aim to provide a comprehensive review of the recent progress in the field. Covering 190 publications, we give an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi-supervised methods and the fully-supervised methods. In addition, we review the existing influential datasets and evaluation metrics. We also suggest some design choices and research directions for future research in image segmentation.