Artificial Multi-Bee-Colony Algorithm for k-Nearest-Neighbor Fields Search

Yunhai Wang, Yiming Qian, Yang Li, Minglun Gong, Wolfgang Banzhaf

Abstract: Searching the k-nearest matching patches for each patch in an input image, i.e., computing the k-nearest-neighbor fields ($k$-NNF), is a core part of various computer vision/graphics algorithms. In this paper, we show that $k$-NNF can be efficiently computed using a novel artificial multi-bee-colony (AMBC) algorithm, where each patch uses a dedicated bee colony to search for its k-nearest matches. As a population-based algorithm, AMBC is capable of escaping local optima. The added communication among different colonies further allows good matches to be quickly propagated across the image. In addition, AMBC makes no assumption about the neighborhood structure or communication direction, making it directly applicable to image sets and suitable for parallel processing. Quantitative evaluations show that AMBC can find solutions that are much closer to the ground truth than the generalized PatchMatch algorithm does. It also outperforms the PatchMatch Graph over image sets.