User tailored colorization using automatic scribbles and hierarchical features

Abstract

Fully automatic image colorization is extensively developed using a large number of examples. However, this kind of methods need further detailed revision if users require fine colorization for delicate objects. This paper proposes a novel scribble-based colorization scheme which automatically generates scribbles and propagates color information from representative pixels within homogeneous superpixels. The scribbles are placed in the regions with minimal entropy to ensure color propagation from representative pixels with high confidence. To avoid color bleeding and under colorization, colors are propagated under a robust similarity metric using a set of hierarchical features, where the low level features consist of quaternion Gabor phases and the high level features are extracted using VGG-19 pre-trained deep model. Experimental results on open dataset demonstrate that the proposed colorization scheme can achieve more consistent results with the users' purposes.