Screen Content Image Segmentation Using Sparse-Smooth Decomposition
This incremental method addresses segmentation for applications like text extraction and video compression, but is domain-specific to image processing.
The paper tackles screen content image segmentation by using a sparse-smooth decomposition technique to separate background and foreground, achieving superior performance over methods like hierarchical k-means clustering in DjVu on HEVC test sequences.
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.