Colorization, the process of adding color to monochrome images, is a tedious and difficult task and often requires intensive manual effort by color experts. To alleviate this problem, a number of computational studies have been proposed in the literature which aim to perform this task in a relatively easy way, either by employing minimal user input in terms of color scribbles or using a colored reference image. Our goal in this paper is to explore a fully-automatic approach to image colorization. In particular we present a novel data-driven strategy which automatically selects the most similar reference image from a large set of color images and utilizes dense correspondences to transfer the color information from the reference image to the input image. We evaluate the performance of our approach on a variety of natural images and obtain fairly good results.