The Application Of Canonical Correlation Analysis In Digital Image Processing

Canonical Correlation Analysis (CCA) is a important research subject of multivariate statistical analysis. With the idea of principal component analysis, CCA reflects the linear correlation between two sets of variables with a few basis vectors. Now, the method has been applied to many fields for correlation analysis and forecast analysis, But there is few relevant reference about applying it to the digital image processing.Firstly the linear algorithms and the nonlinear algorithms of CCA are presented in this paper. Secondly, some experiments have been designed to summarize and compare the characteristics of various kinds of algorithms. Their feasibility and validity in digital image processing is confirmed by the experiments and theory. Finally, to apply the method in multiple classifiers, the combination of multiple classifiers based on belief value is performed.An image segmentation algorithm based on contextual information is proposed in this paper. Firstly, the high-dimensional feature vectors of pixels are extracted through the contextual information. Then, the LDA transformation matrix is improved with CCA, which makes the reduction of dimensions adaptive. Finally, the labels of low-dimensional vectors are obtained using 1 nearest neighbor classifier. The experiments were conducted on face pictures; the result shows that the pixel classification is excellent.