# Image Segmentation using Unsupervised Watershed Algorithm with an Over-segmentation Reduction Technique

arXiv: https://arxiv.org/abs/1810.03908 · HTML: https://mlsanity.com/paper/1810.03908/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

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

- [A comparative study of source-finding techniques in HI emission line cubes using SoFiA, MTObjects, and supervised deep learning](https://mlsanity.com/paper/2211.12809/) (2022)

## Abstract (arXiv)

Image segmentation is the process of partitioning an image into meaningful segments. The meaning of the segments is subjective due to the definition of homogeneity is varied based on the users perspective hence the automation of the segmentation is challenging. Watershed is a popular segmentation technique which assumes topographic map in an image, with the brightness of each pixel representing its height, and finds the lines that run along the tops of ridges. The results from the algorithm typically suffer from over segmentation due to the lack of knowledge of the objects being classified. This paper presents an approach to reduce the over segmentation of watershed algorithm by assuming that the different adjacent segments of an object have similar color distribution. The approach demonstrates an improvement over conventional watershed algorithm.