A sparse coding method for semi-supervised segmentation with multi-class histogram constraints

We present a semi-supervised segmentation method that uses a dictionary of multi-class foreground histograms to enhance the segmentation in the presence of incorrect or missing labels. Instead of requiring a target histogram, or a set of images with the same foreground, this method uses sparse coding to find the most relevant histogram for the foreground. An efficient strategy based on the ADMM algorithm is proposed to avoid the problems of non-submodularity and non-linearity, normally related to histogram-based segmentation. Experiments on the segmentation of natural images with incomplete or incorrect labels show our method to be more robust and accurate than other approaches for this task.