When dealing with color images, early researchers treated the three color channels as independent grayscale images, by concatenating the three channels into a single long vector.38 Under this simple representation, the spatial relationships that exist between the color pixels are destroyed, and the dimension of the image becomes three times that of the classical grayscale model. Furthermore, research has shown that there is high interchannel correlation among the RGB channels,39 and therefore simple concatenation results in redundancy. As such, several efficient techniques of incorporating color channels have been suggested. The i1i2i340 color transform has been used in the past to decorrelate the RGB channels using Karhunen–Loève transform.39,41 Recently, quaternion, a powerful mathematical tool, has been applied to the problem.42,43 This has proven to be a good feature extraction method due to its ability to preserve the spatial relationships among R, G, and B channels. Additionally, it retains the holistic properties of PCA. Also, quaternion algebra has been applied to complex-type moments for color images43 and has been shown to be invariant to image rotation, scale, and translation transformations. However, the method still works in an unsupervised manner, and hence does not take into consideration the class labels of the response variables.