A graph-cut approach to image segmentation using an affinity graph based on ℓ0-sparse representation of features

We propose a graph-cut based image segmentation method by constructing an affinity graph using ℓ0 sparse representation. Computing first oversegmented images, we associate with all segments, that we call superpixels, a collection of features. We find the sparse representation of each set of features over the dictionary of all features by solving a ℓ0-minimization problem. Then, the connection information between superpixels is encoded as the non-zero representation coefficients, and the affinity of connected superpixels is derived by the corresponding representation error. This provides a ℓ0 affinity graph that has interesting properties of long range and sparsity, and a suitable graph cut yields a segmentation. Experimental results on the BSD database demonstrate that our method provides perfectly semantic regions even with a constant segmentation number, but also that very competitive quantitative results are achieved.