  Image learning and colorization are hot spots in multimedia domain. Inspired
by the learning capability of humans, in this paper, we propose an automatic
colorization method with a learning framework. This method can be viewed as a
hybrid of exemplar-based and learning-based method, and it decouples the
colorization process and learning process so as to generate various color
styles for the same gray image. The matching process in the exemplar-based
colorization method can be regarded as a parameterized function, and we employ
a large amount of color images as the training samples to fit the parameters.
During the training process, the color images are the ground truths, and we
learn the optimal parameters for the matching process by minimizing the errors
in terms of the parameters for the matching function. To deal with images with
various compositions, a global feature is introduced, which can be used to
classify the images with respect to their compositions, and then learn the
optimal matching parameters for each image category individually. What's more,
a spatial consistency based post-processing is design to smooth the extracted
color information from the reference image to remove matching errors. Extensive
experiments are conducted to verify the effectiveness of the method, and it
achieves comparable performance against the state-of-the-art colorization
algorithms.
