Application of PCA in dimension reduction of image Zernike moments feature
主成分分析在图像Zernike矩特征降维中的应用

Higher dimension of image feature is the critical problem and the dimension reduction is the most important phase in image processing. It was pointed out that the dimension of Zernike moments feature vector was generally high after briefly introducing the basic concept of the Zernike moments and the image Zernike moments shape feature vector. Based on the principal components analysis, it was shown that the principal components analysis (PCA) could be applied in dimension reduction of image Zernike moments feature. Meanwhile, the process of the dimension reduction based on PCA was put forward. The experimental results demonstrate the feasibility of the application.