Image segmentation by combining the strengths of Relative Fuzzy Connectedness and Graph Cut

We introduce an image segmentation algorithm GC sum max , which combines, in a novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC sum max preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's bigger control over "leaking though the weak boundary." The theoretical analysis of GC sum max is greatly facilitated by our recent theoretical results that RFC belongs to the Generalized GC (GGC) segmentation algorithms framework. In our implementation of GC sum max we use, as a subroutine, a version of RFC algorithm (based on Image Foresting Transform) that runs (provably) in linear time with respect to the image size. This results in GC sum max running in a time close to linear.