Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation

TL;DR

This paper provides a comprehensive survey of recent advances in image segmentation, covering bottom-up, superpixel, interactive, object proposal, semantic parsing, and cosegmentation methods, datasets, and evaluation metrics.

Contribution

It offers an extensive overview of 180 publications, summarizing recent progress, challenges, and future research directions in image segmentation.

Findings

Reviewed recent segmentation algorithms and approaches.

Analyzed influential datasets and evaluation metrics.

Suggested future research directions.

Abstract

Image segmentation refers to the process to divide an image into nonoverlapping meaningful 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. However, while many segmentation algorithms exist, yet there are only a few sparse and outdated summarizations available, an overview of the recent achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in this field. Covering 180 publications, we give an overview of broad areas of segmentation topics including not only the classic bottom-up approaches, but also the recent development in superpixel, interactive methods, object proposals, semantic image parsing and image cosegmentation. In addition, we also review the existing influential datasets and evaluation metrics. Finally, we suggest some design flavors and research directions for future research in image segmentation.