Image colorization is an important and difficult problem in image processing with various
applications including image stylization and heritage restoration. Most existing
image colorization methods utilize feature matching between the reference color image
and the target grayscale image. The effectiveness of features is often significantly
affected by the characteristics of the local image region. Traditional methods usually
combine multiple features to improve the matching performance. However, the same
set of features is still applied to the whole images. In this paper, based on the observation
that local regions have different characteristics and hence different features may
work more effectively, we propose a novel image colorization method using automatic
feature selection with the results fused via a Markov Random Field (MRF) model for
improved consistency. More specifically, the proposed algorithm automatically classifies
image regions as either uniform or non-uniform, and selects a suitable feature
vector for each local patch of the target image to determine the colorization results.
For this purpose, a descriptor based on luminance deviation is used to estimate the
probability of each patch being uniform or non-uniform, and the same descriptor is
also used for calculating the label cost of the MRF model to determine which feature
vector should be selected for each patch. In addition, the similarity between the luminance
of the neighborhood is used as the smoothness cost for the MRF model which enhances the local consistency of the colorization results. Experimental results on a variety
of images show that our method outperforms several state-of-the-art algorithms,
both visually and quantitatively using standard measures and a user study
