基于模板匹配和SVM的草图符号自适应识别方法

Adaptive Symbol Recognition for Sketch-Based Interfaces Based on Template Matching and SVM

在草图符号的自适应学习中,不同用户的训练样本数量可能不同,保持在不同样本数量下良好的学习效果成为需要解决的一个重要问题.提出一种自适应的草图符号识别方法,该方法采用与训练样本个数相关的分类器组合策略将模板匹配方法和SVM统计分类方法进行了高效组合.它通过利用支持小样本学习的模板匹配方法和支持大量样本学习的SVM方法,并同时利用草图符号中的在线信息和离线信息,实现了不同样本个数下自适应的符号学习和识别.基于该方法,文中设计并实现了支持自适应识别的草图符号组件.最后,利用扩展的PIBG Toolkit开发出原型系统Idea Note.评估表明,该方法可以在24类草图符号分别使用1到20个训练样本时具有较高的识别正确率和较好的时间性能.

During adaptive learning of symbols in sketch-based interfaces, the number of training samples may be different for different users and it is challenging for recognition methods to learn with flexible sample numbers. This paper proposes an adaptive symbol recognition method for sketch-based interfaces. It combines template matching method that could learn with few samples and SVM method that could learn with more samples by a strategy related to sample numbers. Both online information and offline information are utilized. Thus it could learn and recognize with different sample numbers. Based on the proposed method, the authors build a symbol widget that supports adaptive recognition. At last, a prototype system, IdeaNote, is built based on the extended PIBG Toolkit. Evaluation shows that when there are 24 kinds of symbols, the method could achieve high recognition accuracy and good time performance with 1 to 20 training samples.