A Graph Cut Approach to Image Segmentation in Tensor Space

This paper proposes a novel method to apply the standard graph cut technique to segmenting multimodal tensor valued images. The Riemannian nature of the tensor space is explicitly taken into account by first mapping the data to a Euclidean space where nonparametric kernel density estimates of the regional distributions may be calculated from user initialized regions. These distributions are then used as regional priors in calculating graph edge weights. Hence this approach utilizes the true variation of the tensor data by respecting its Riemannian structure in calculating distances when forming probability distributions. Further, the non-parametric model generalizes to arbitrary tensor distribution unlike the Gaussian assumption made in previous works. Casting the segmentation problem in a graph cut framework yields a segmentation robust with respect to initialization on the data tested.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Allen Tannenbaum, James G. Malcolm, Yogesh Rathi
• Keywords: Computer Vision, CVPR 2007, Graph Cut Framework, Graph Cut Technique, Tensor Data, Tensor Space, Tensor Valued Images