This work presents a novel approach to this task, using a bag of visual words approach to represent and classify en-face optical sections from four distinct strata of the skin. A dictionary of representative features is learned from whitened and normalised patches using hierarchical spherical k-means. Each image is then represented by extracting a dense array of patches and encoding each with the most similar element in the dictionary. Linear discriminant analysis is used as a simple linear classifier.
The proposed framework was tested on 308 depth stacks from 54 volunteers. Parameters are tuned using 10 fold cross validation on a training sub-set of the data, and final evaluation was performed on a held out test set.
The proposed method generated physically plausible profiles of the distinct strata of human skin, and correctly classified 81.4% of sections in the test set.