Selective Regional Correlation for Pattern Recognition
Selective Regional Correlation for Pattern Recognition
复制标题
DOI：
10.1109/tsmca.2006.886333
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
E. Sejdić;Jin Jiang
中科院分区：
文献类型：
--
作者：
E. Sejdić;Jin Jiang
本文提出了一种新的基于相关性的模式分类器，它依赖于模板和信号的时频分解分析。与传统的基于相关性的分类器相比，使用这种新的分类器在分辨率和准确性方面得到了显着的改善。在时频分解过程中考虑了短时傅立叶变换、连续小波变换和S变换。为了评估所提出的方案的性能，数值研究进行了一组合成的测试信号，并取得了良好的结果。本文还提出了一个说明性的例子，两种类型的心音进行分类。新分类器对心音的分类错误率仅为6.670%，而基于相关性的分类器的分类错误率为56.67
In this paper, a novel correlation-based pattern classifier that relies on the analysis of time-frequency decomposition of a template and signals is proposed. Significant improvements in resolution and accuracy are obtained using this new classifier when compared to a conventional correlation-based one. The short-time Fourier transform, continuous wavelet transform, and S-transform are considered in the time-frequency decomposition process. To evaluate the performance of the proposed scheme, numerical studies are performed on a set of synthetic test signals, and excellent results have been obtained. This paper also presents an illustrative example where two types of heart sounds are classified. The classification error percentage for the heart sounds using the new classifier is only 6.670% as compared to 56.67% when a general correlation-based classifier is used