Cartoon synthesis using constrained spreading activation network

In this paper, we propose an approach to synthesize cartoons from the existing cartoon data by controlling the character's path which is defined by the cartoonists in a background image. First, detailed pre-experiments are conducted in which different cartoon features are extracted and compared. During the pre-experiments, three features extracted from edge, motion and color are demonstrated effectively for evaluating cartoon similarity according to the quantitative analysis. The three features are then fused and a Cartoon Frame Relationship Network is constructed. Based on the graph, we propose a Constrained Spreading Activation Algorithm to select candidate frames which are visually similar to the current frame to generate the next frame. The cartoons are synthesized by choosing the most appropriate frame from the candidates in accordance with the path designed by the cartoonists. When the new cartoons are applied into the background image, our approach coordinates the cartoon character's size according to the image's perspective as well. The experiment results demonstrate that the combination of the three proposed features are effective in similarity evaluation, and the candidates selected by Constrained Spreading Activation Algorithm, are more similar to the current frame compared with other algorithms. The results also show that our approach can synthesize visually smooth cartoons from the existing cartoon library.