  This paper investigates the problem of image segmentation using superpixels.
We propose two approaches to enhance the discriminative ability of the
superpixel's covariance descriptors. In the first one, we employ the
Log-Euclidean distance as the metric on the covariance manifolds, and then use
the RBF kernel to measure the similarities between covariance descriptors. The
second method is focused on extracting the subspace structure of the set of
covariance descriptors by extending a low rank representation algorithm on to
the covariance manifolds. Experiments are carried out with the Berkly
Segmentation Dataset, and compared with the state-of-the-art segmentation
algorithms, both methods are competitive.
