# Screen Content Image Segmentation Using Sparse-Smooth Decomposition

arXiv: https://arxiv.org/abs/1511.06911 · HTML: https://mlsanity.com/paper/1511.06911/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2015.

## Built on by (most cited first, 2 of 2)

- [PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation](https://mlsanity.com/paper/2203.14457/) (2022)
- [Compressed Smooth Sparse Decomposition](https://mlsanity.com/paper/2201.07404/) (2022)

## Abstract (arXiv)

Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.