I'm trying to figure out how to use PCA to decorrelate an RGB image in python. I'm using the code found in the O'Reilly Computer vision book:
from PIL import Image from numpy import * def pca(X): # Principal Component Analysis # input: X, matrix with training data as flattened arrays in rows # return: projection matrix (with important dimensions first), # variance and mean #get dimensions num_data,dim = X.shape #center data mean_X = X.mean(axis=0) for i in range(num_data): X[i] -= mean_X if dim>100: print 'PCA - compact trick used' M = dot(X,X.T) #covariance matrix e,EV = linalg.eigh(M) #eigenvalues and eigenvectors tmp = dot(X.T,EV).T #this is the compact trick V = tmp[::-1] #reverse since last eigenvectors are the ones we want S = sqrt(e)[::-1] #reverse since eigenvalues are in increasing order else: print 'PCA - SVD used' U,S,V = linalg.svd(X) V = V[:num_data] #only makes sense to return the first num_data #return the projection matrix, the variance and the mean return V,S,mean_X I know I need to flatten my image, but the shape is 512x512x3. Will the dimension of 3 throw off my result? How do I truncate this? How do I find a quantitative number of how much information is retained?