Image Segmentation Using Overlapping Group Sparsity
This is an incremental improvement for screen content coding applications, enhancing segmentation accuracy in specific image types.
The paper tackles image segmentation into background and foreground text/graphics by using sparse decomposition with smoothness and connectivity priors, and it outperforms prior methods like least absolute deviation fitting and k-means clustering on a dataset from HEVC test sequences.
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.