# Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories

arXiv: https://arxiv.org/abs/2104.10762 · HTML: https://mlsanity.com/paper/2104.10762/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Comparing LBP, HOG and Deep Features for Classification of Histopathology Images](https://mlsanity.com/paper/1805.05837/) (2018)
- [HOG based Fast Human Detection](https://mlsanity.com/paper/1501.02058/) (2015)
- [A Fast HOG Descriptor Using Lookup Table and Integral Image](https://mlsanity.com/paper/1703.06256/) (2017)
- [HOG feature extraction from encrypted images for privacy-preserving machine learning](https://mlsanity.com/paper/1904.12434/) (2019)
- [A Critical Connectivity Radius for Segmenting Randomly-Generated, High Dimensional Data Points](https://mlsanity.com/paper/1602.03822/) (2016)
- [Auto-encoding a Knowledge Graph Using a Deep Belief Network: A Random Fields Perspective](https://mlsanity.com/paper/1911.06322/) (2019)

## Abstract (arXiv)

Random field and random cluster theory are used to describe certain mathematical results concerning the probability distribution of image pixel intensities characterized as generic 2D integer arrays. The size of the smallest bounded region within an image is estimated for segmenting an image, from which, the equilibrium distribution of intensities can be recovered. From the estimated bounded regions, properties of the sub-optimal and equilibrium distributions of intensities are derived, which leads to an image compression methodology whereby only slightly more than half of all pixels are required for a worst-case reconstruction of the original image. A custom deep belief network and heuristic allows for the unsupervised segmentation, detection and localization of objects in an image. An example illustrates the mathematical results.