MATLAB Implementation of Canonical Correlation Analysis (CCA)

This MATLAB code implements Canonical Correlation Analysis (CCA) to analyze the correlation between two input images. The input parameters include: input image 1, input image 2, rows, columns, channel 1, channel 2, and the output matrix. During the canonical correlation analysis process, the code first preprocesses input image 1 and input image 2 by adjusting their size and resolution through MATLAB's image processing functions like imresize(). The code then extracts required features from both images based on the specified row, column, and channel parameters using matrix indexing operations. These extracted features are formatted into appropriate data structures for subsequent correlation analysis. The implementation utilizes the canonical correlation analysis algorithm which computes the linear relationship between the two sets of image features. The core algorithm involves calculating covariance matrices and solving eigenvalue problems to find the canonical correlations. This process helps identify the underlying relationships and patterns between the two images, revealing both similarities and differences through mathematical correlation coefficients. The final output is a matrix containing the canonical correlation analysis results between input image 1 and input image 2. This output matrix can be used for further data analysis and visualization through MATLAB's plotting functions, enabling better understanding of the relationship between the two images. Thus, by using this CCA implementation code, users can perform canonical correlation analysis on two images and obtain correlation results that facilitate deeper understanding of the relationship between the images through quantitative correlation measures.