A k-partition, graph theoretic approach to perceptual organization

We present an k-partition, graph theoretic approach to perceptual organization. Principal results include a generalization of the bipartition normalized cut to a k-partition measure, and a derivation of a suboptimal, polynomial time solution to the NP-hard k-partition problem. The solution is obtained by first relaxing to an eigenvalue problem, followed by a heuristic procedure to enforce feasible solutions. This approach is a departure from the standard k-partitioning graph literature in that the partition measure used is nonquadratic, and is a departure from image segmentation literature in that k-partitioning is used in place of a recursive bipartition. We apply this approach to image segmentation of infrared (IR) images, and show representative segmentation results. Initial results show promise for further investigation.