Improving superpixel-based image segmentation by incorporating color covariance matrix manifolds

We propose to use color covariance matrices of superpixels as a feature in addition to colors. A non-Euclidean distance metric is employed for the covariance matrix manifolds. We then introduce three ways of fusing the similarity matrices obtained from both feature spaces for affinity graph generation. Experiments carried out using a benchmark dataset reveals that our approach achieves competitive and even better results compared with the state of the art.