# Chan-Vese Reformulation for Selective Image Segmentation

arXiv: https://arxiv.org/abs/1811.08751 · HTML: https://mlsanity.com/paper/1811.08751/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [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)
- [Using Deep Image Prior to Assist Variational Selective Segmentation Deep Learning Algorithms](https://mlsanity.com/paper/2112.00793/) (2021)
- [Cartoon-texture evolution for two-region image segmentation](https://mlsanity.com/paper/2203.03513/) (2022)

## Abstract (arXiv)

Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type. Typically, such methods involve introducing additional constraints to generic segmentation approaches. However, we show that this is often inconsistent with respect to common assumptions about the image. The proposed method introduces a new fitting term that is more useful in practice than the Chan-Vese framework. In particular, the idea is to define a term that allows for the background to consist of multiple regions of inhomogeneity. We provide comparitive experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods.