Image Segmentation Using Overlapping Group Sparsity

TL;DR

This paper introduces a novel sparse decomposition algorithm for image segmentation that effectively separates background and foreground elements by modeling the background with smooth basis functions and the foreground as a sparse component, outperforming prior methods.

Contribution

The paper proposes a new sparse decomposition-based segmentation algorithm that incorporates prior information to improve separation of background and foreground in images.

Findings

Outperforms prior segmentation methods on HEVC screen content images.

Effectively models background with smooth basis functions.

Separates foreground as a sparse, connected component.

Abstract

Sparse decomposition has been widely used for different applications, such as source separation, image classification and image denoising. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition. First, the background is represented using a suitable smooth model, which is a linear combination of a few smoothly varying basis functions, and the foreground text and graphics are modeled as a sparse component overlaid on the smooth background. Then the background and foreground are separated using a sparse decomposition framework and imposing some prior information, which promote the smoothness of background, and the sparsity and connectivity of foreground pixels. This algorithm has been tested on a dataset of images extracted from HEVC standard test sequences for screen content coding, and is shown to outperform prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu, and shape primitive extraction and coding algorithm.