  This paper considers how to separate text and/or graphics from smooth
background in screen content and mixed content images and proposes an algorithm
to perform this segmentation task. The proposed methods make use of the fact
that the background in each block is usually smoothly varying and can be
modeled well by a linear combination of a few smoothly varying basis functions,
while the foreground text and graphics create sharp discontinuity. This
algorithm separates the background and foreground pixels by trying to fit pixel
values in the block into a smooth function using a robust regression method.
The inlier pixels that can be well represented with the smooth model will be
considered as background, while remaining outlier pixels will be considered
foreground. We have also created a dataset of screen content images extracted
from HEVC standard test sequences for screen content coding with their ground
truth segmentation result which can be used for this task. The proposed
algorithm has been tested on the dataset mentioned above and is shown to have
superior performance over other methods, such as the hierarchical k-means
clustering algorithm, shape primitive extraction and coding, and the least
absolute deviation fitting scheme for foreground segmentation.
