Image segmentation via edge contour finding: a graph theoretic approach

A graph-theoretic approach for image segmentation is presented. The pixels of the image are represented by the vertices of an undirected adjacency graph G. All neighboring pairs of pixels are linked by arcs with capacities assigned to reflect the strength of an edge element between the linked vertices. Segmentation is achieved by removing arcs corresponding to selected minimum cuts of G to form mutually exclusive subgraphs such that the largest intersubgraph maximum flow is minimized. This is equivalent to partitioning the image using closed contours of edge elements, which consist mostly of strong edges. The method accurately locates region boundaries and at the same time rejects contours containing isolated strong edges. The minimum cuts in G can be computed from a partially cut-equivalent tree of G. A fast algorithm for constructing partially equivalent trees that can handle graphs with several hundred thousand vertices is developed