@Article{iasc.2020.010118,
AUTHOR = {Hui Li, Wei Zeng, Guorong Xiao, Huabin Wang},
TITLE = {The Instance-Aware Automatic Image Colorization Based on Deep Convolutional Neural Network},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {841--846},
URL = {http://www.techscience.com/iasc/v26n4/40288},
ISSN = {2326-005X},
ABSTRACT = {Recent progress on image colorization is substantial and benefiting mostly from 
the great development of the deep convolutional neural networks. However, 
one type of object can be colored by different kinds of colors. Due to the 
uncertain relationship between the object and color, the deep neural network is 
unstable and difficult to converge during the training process. In order to solve 
this problem, this paper proposes an instance-aware automatic image 
colorization algorithm, which uses the semantic features of the object instance 
as prior knowledge to guide the deep neural network to do the colorization 
task. Meanwhile, we design a discrete loss function to train the deep network 
and this network can be trained from end to end. Experiments show that this 
algorithm can obtain satisfactory colorful results on the images containing 
object instance and achieves state-of-the-art results.},
DOI = {10.32604/iasc.2020.010118}
}