Image segmentation through energy minimization based subspace fusion

We present an image segmentation technique that fuses contributions from multiple feature subspaces using an energy minimization approach. For each subspace, we compute a per-pixel quality measure and perform a partitioning through the standard normalized cut algorithm. To fuse the subspaces into a final segmentation, we compute a subspace label for every pixel. The labeling is computed through the graph-cut energy minimization framework proposed by Boycov, Y., et al. (2001). Finally, we combine the initial subspace segmentation with the subspace labels obtained from the energy minimization to yield the final segmentation. We have implemented the algorithm and provide results for both synthetic and real images.