# Image Labeling with Markov Random Fields and Conditional Random Fields

arXiv: https://arxiv.org/abs/1811.11323 · HTML: https://mlsanity.com/paper/1811.11323/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Nuclei Instance Segmentation of Cryosectioned H&E Stained Histological Images using Triple U-Net Architecture](https://mlsanity.com/paper/2404.12986/) (2024)

## Abstract (arXiv)

Most existing methods for object segmentation in computer vision are formulated as a labeling task. This, in general, could be transferred to a pixel-wise label assignment task, which is quite similar to the structure of hidden Markov random field. In terms of Markov random field, each pixel can be regarded as a state and has a transition probability to its neighbor pixel, the label behind each pixel is a latent variable and has an emission probability from its corresponding state. In this paper, we reviewed several modern image labeling methods based on Markov random field and conditional random Field. And we compare the result of these methods with some classical image labeling methods. The experiment demonstrates that the introduction of Markov random field and conditional random field make a big difference in the segmentation result.