Propagation Based Prototype Prediction

The prediction phase is used to interact with end users, so its response speed is critical for a good user experience to large category recognition tasks. This paper presents a novel and fast algorithm for prototype prediction which may solve the current computing challenges in character input applications on smart terminals. We construct a social network for prototypes and their pair-wise connections. Such "prototype network" falls into "scale-free network", emerging "small-world effect". Under reasonable conditions, there exists a small geodesic path between each node pair. This feature guarantees us to search "better" nodes following the directed edges. Unfortunately, the naive search strategy results in a computing complexity of exponential order. To convert the problem to a manageable scale, we enhance the generic breadth-first search with greedy selectivity. As a result, our method just consider a small candidate set and further propagate from those seeds. Thorough analysis both on network structure and algorithmic propagation patterns is conducted and advantages in efficiency and practicality are revealed. Finally, we evaluated the proposed algorithm on a large-scale, large-category handwritten Chinese character recognition task. Experimental results show that the proposed algorithm can be tuned with either faster prediction speed or higher prediction accuracy.