Multi-source Color Transfer Based on Multi-labeled Decision Tree

At present, most color transfer algorithms are based on single source image, failing to meet reference color demand of object image. This paper proposes a multi-source color transfer algorithm based on multi-labeled decision tree. At first, we define an image as a multi-labeled set since it contains multiple objects. Then every source image is divided into several sub-images to form the training samples for the decision tree. Through extracting color and texture features from sub-image, training dataset is formed and the corresponding label set is obtained by amended K-mean clustering. After that, a multi-labeled decision tree is constructed using SCC_SP. Finally, color transfer is performed on object image based on its predicted label set by the built tree. Experiment results demonstrate that the proposed algorithm works well on multi-source color transfer, making up the shortage of color transfer based on single source image.