Fast Image Segmentation using Region Merging with a k-Nearest Neighbor Graph

A fast region merging method is proposed for solving the image segmentation problem. Rather than focusing on the global features of the image, our attention is drawn to local relationship between neighbor pixels with the goal that all similar pixels should be segmented in the same region. In this paper, the image segmentation problem is treated as a region merging procedure. To solve the problem, an initial oversegmentation is performed on the image and a k-Nearest Neighbor (k-NN) Graph whose vertexes denote regions is built. A new region similarity measure function is also proposed and the region similarity is assigned to the edge as its weight, which can make use of pixel intensity, edge feature, texture and so forth in a unit form. In k-NN graph, each vertex chooses exactly k nearest neighbors to connect. With it, the computation complexity of merging process can be reduced to 0(tau N log(2) N); here, tau denotes the number of nearest neighbor updates required at each iteration while N denotes the number of the initial regions. Implementation of the proposed algorithm is introduced, and some experiment results are given to prove our method's robustness and efficiency.