The term PCA Color Augmentation refers to a type of data augmentation technique first mentioned in the paper titled ImageNet Classification with Deep Convolutional Neural Networks. This paper is famous for introducing the AlexNet convolutional neural network architecture, which won the 2012 ImageNet Large Scale Visual Recognition Competition.

In general, data augmentation is the process of increasing the size of a dataset by transforming it in ways that a neural network is unlikely to learn by itself. For example, image-recognition datasets often train with images flipped vertically or horizontally.

Another form of data augmentation on image datasets is to alter the color balance of the image–for example, adjusting the values of the red, green, and blue pixels in the image.

Specifically, PCA Color Augmentation is designed to shift those values based on which values are the most present in the image. Images with heavy red values and minimal green values will have their red values altered the most through PCA Color Augmentation.

The specific mechanism relies on the principal component analysis (PCA) algorithm to find the relative color balance of a given image.

The AlexNet paper describes PCA Color Augmentation in this paragraph:

Specifically, we perform PCA on the set of RGB pixel values throughout the ImageNet training set. To each training image, we add multiples of the found principal components, with magnitudes proportional to the corresponding eigenvalues times a random variable drawn from a Gaussian with mean zero and standard deviation 0.1. Therefore to each RGB image pixel \(I_{xy} = \left [ I^R_{xy}, I^G_{xy}, I^B_{xy} \right ]^T\), we add the following quantity:

\[\left [ \mathbf p_1, \mathbf p_2, \mathbf p_3 \right ] \left [ \alpha_1 \lambda_1, \alpha_2 \lambda_2, \alpha_3 \lambda_3 \right ]^T\]