Building a Compact MQDF Classifier by Sparse Coding and Vector Quantization Technique
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)(2017)
摘要
The modified quadratic discriminant function (MQDF) is a very popular handwritten Chinese character classifier thanks to its high performance with low computational complexity. However, it suffers from high memory requirement for the storage of its parameters. This paper proposes a compact MQDF classifier developed by integrating sparse coding and vector quantization (VQ) technique. To be specific, we use sparse coding to represent the parameters of MQDF in sparsity first, and then employ the VQ technique to further compress the sparse coding. The proposed method is evaluated by comparing the performance with three models, i.e., the original MQDF classifier, the compact MQDF classifier using the VQ technique, and the compact MQDF classifier using sparse coding. The effectiveness of our proposed approach has been confirmed and demonstrated by comparative experiments on ICDAR2013 competition dataset.
更多查看译文
关键词
compact MQDF classifier,sparse coding,vector quantization technique
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要