EvaToon: A novel graph matching system for evaluating cartoon drawings

Imitation cartoon drawing is an important skill for cartoonists, requiring quantity of efforts on practising and guidance. In this paper, we propose EvaToon, an imitated drawing evaluate system, which automatically assigns judging scores and marks improper drawing regions. With our system, cartoonists can practise and get guidance by themselves. We have cooperated with several experts on developing such an evaluation system. Based on their guide, we present EvaToon in two stages comprising cartoon drawings analyzing and similarity evaluating. During analyzing, we first locate contour pixels with high curvature as interest points and then extract multi-scale features around interest points to hierarchically describe shape. During evaluating, we first match interest points between original and imitated drawing based on distance of features. After matching, we construct a regression tree to map high dimensional difference of matching features to scores and marks based on quantity of manually evaluated training examples. Finally, our system matches an input imitated drawing with the original one and predicts its scores automatically. We demonstrate the accuracy of our EvaToon system in matching and predicting and prove the capability of describing shape of our proposed features by experiments on a collected dataset of imitated drawings.