Retrieval Based Cartoon Synthesis via Heterogeneous Features Learning

In this paper, we present a novel gesture recognition method for synthesizing cartoons from existing two dimensional cartoon data. Drawing inspiration from cross media community, human-subject images are acted as the queries here to retrieve cartoon images containing similar gestures. Optimal descriptors are assigned to express features of cartoon and human-subject images based on their characteristics, and they are defined as heterogeneous features for different dimensions. Inspired of exploiting data structure in manifold learning problem, we integrate heterogeneous dimensionality reduction and linear discriminant model into a hierarchical framework. Cartoon synthesis can be carried out based on the retrieved cartoon key frames. Experiments and the application demonstrate the effectiveness of our proposed method.