Improving Handwritten Chinese Character Recognition with Discriminative Quadratic Feature Extraction
ICPR(2014)
摘要
Discriminative feature extraction (DFE) is an effective linear dimensionality reduction method for pattern recognition. It improves the recognition performance via optimizing subspace projection axes and classifier parameters simultaneously. In this paper, we propose a nonlinear extension of DFE, called discriminative quadratic feature extraction (DQFE), for which feature vectors are firstly mapped to a high-dimensional nonlinear space and then projected to a low-dimensional subspace learned by DFE. The nonlinear mapping is obtained by adding quadratic (correlation or covariance) features computed directly on the original gradient feature maps with different region partition. In this way, both the structural information of the image and the correlation information of features are used to generate a nonlinear high-dimensional feature mapping (thousands of dimensions). Experimental results demonstrated that DQFE can improve the accuracy for different classifiers in handwritten Chinese character recognition.
更多查看译文
关键词
pattern recognition,correlation features,covariance analysis,region partition,dfe,dqfe,recognition performance improvement,structural information,gradient feature maps,covariance features,feature extraction,image classification,discriminative quadratic feature extraction,nonlinear high-dimensional feature mapping,handwritten character recognition,classifier parameters,linear dimensionality reduction method,feature vector mapping,subspace projection axis optimization,correlation methods,handwritten chinese character recognition,low-dimensional subspace,high-dimensional nonlinear space
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络