  We propose an algorithm for segmenting natural images based on texture and
color information, which leverages the co-sparse analysis model for image
segmentation within a convex multilabel optimization framework. As a key
ingredient of this method, we introduce a novel textural similarity measure,
which builds upon the co-sparse representation of image patches. We propose a
Bayesian approach to merge textural similarity with information about color and
location. Combined with recently developed convex multilabel optimization
methods this leads to an efficient algorithm for both supervised and
unsupervised segmentation, which is easily parallelized on graphics hardware.
The approach provides competitive results in unsupervised segmentation and
outperforms state-of-the-art interactive segmentation methods on the Graz
Benchmark.
