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

arXiv: https://arxiv.org/abs/1502.00717 · HTML: https://mlsanity.com/paper/1502.00717/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2015.

## Built on by (most cited first, 10 of 10)

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- [Super Neurons](https://mlsanity.com/paper/2109.01594/) (2021)
- [3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting](https://mlsanity.com/paper/2404.17273/) (2024)
- [LNSMM: Eye Gaze Estimation With Local Network Share Multiview Multitask](https://mlsanity.com/paper/2101.07116/) (2021)
- [Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation](https://mlsanity.com/paper/2110.11685/) (2021)
- [Harmonizing output imbalance for defect segmentation on extremely-imbalanced photovoltaic module cells images](https://mlsanity.com/paper/2211.05295/) (2022)
- [Falcon: Fractional Alternating Cut with Overcoming Minima in Unsupervised Segmentation](https://mlsanity.com/paper/2504.05613/) (2025)
- [Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper](https://mlsanity.com/paper/2603.04796/) (2026)

## Abstract (arXiv)

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 However, while many segmentation algorithms exist, yet there are only a few sparse and outdated summarizations available, an overview of the recent achievements and issues is lacking.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.