一类多频带主分量分析方法

Multi-Band Principal Component Analysis Method

主分量分析是模式识别领域使用较广的一种特征抽取方法,但是由于经典的主分量分析在处理图像矩阵时需要将图像展开成向量形式,使得计算量很大。本文提出了一种多频带主分量分析方法,该方法不仅减少了运算过程中的计算量,而且在一定程度上提高了整体性能。首先通过二维离散余弦变换将图像转变成频率数据,再按照频率变化将数据分成多个频带,然后在此基础上设计了针对多个频带数据的主分量分析方法。通过对ORL和NUST603图像库进行实验证明,本文方法不仅具有快速提取图像特征的能力,而且综合性能优于相应的主分量分析。

Principal component analysis （PCA） is the well-known method in pattern recognition. However, expanding original image matrices into the same dimensional vectors in classical PCA increase the computational complexity. Here one presents a kind of multi-band principle component analysis （MBPCA）. The process can reduce the computational complexity thus improving the overall performance. Firstly, the image is transformed into frequency data by the two-dimensional discrete cosine transform. Secondly, frequency data is divided into a plurality of frequency bands according to its frequency range. Finally, a principal component analysis method using a plurality of frequency bands is designed. The experiments on ORL and NUST603 face database show that the proposed method has the ability to quickly extract image features and performs better than the corresponding principal component analysis.