# Post Processing of image segmentation using Conditional Random Fields

arXiv: https://arxiv.org/abs/2510.09833 · HTML: https://mlsanity.com/paper/2510.09833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [W-Net: A Deep Model for Fully Unsupervised Image Segmentation](https://mlsanity.com/paper/1711.08506/) (2017)
- [Recent progress in semantic image segmentation](https://mlsanity.com/paper/1809.10198/) (2018)
- [EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/1804.00292/) (2018)
- [Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation](https://mlsanity.com/paper/1711.04483/) (2017)

## Abstract (arXiv)

The output of image the segmentation process is usually not very clear due to low quality features of Satellite images. The purpose of this study is to find a suitable Conditional Random Field (CRF) to achieve better clarity in a segmented image. We started with different types of CRFs and studied them as to why they are or are not suitable for our purpose. We evaluated our approach on two different datasets - Satellite imagery having low quality features and high quality Aerial photographs. During the study we experimented with various CRFs to find which CRF gives the best results on images and compared our results on these datasets to show the pitfalls and potentials of different approaches.