# Image Segmentation Algorithms Overview

arXiv: https://arxiv.org/abs/1707.02051 · HTML: https://mlsanity.com/paper/1707.02051/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Self-semantic contour adaptation for cross modality brain tumor segmentation](https://mlsanity.com/paper/2201.05022/) (2022)
- [CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation](https://mlsanity.com/paper/2110.02417/) (2021)
- [Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones](https://mlsanity.com/paper/2107.03461/) (2021)
- [Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentation](https://mlsanity.com/paper/2110.02196/) (2021)
- [Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods](https://mlsanity.com/paper/2201.07931/) (2022)
- [Robust Isolation Forest using Soft Sparse Random Projection and Valley Emphasis Method](https://mlsanity.com/paper/2503.12125/) (2025)

## Abstract (arXiv)

The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms.