KL divergence based agglomerative clustering for automated Vitiligo grading

In this paper we present a symmetric KL divergence based agglomerative clustering framework to segment multiple levels of depigmentation in Vitiligo images. The proposed framework starts with a simple merge cost based on symmetric KL divergence. We extend the recent body of work related to Bregman divergence based agglomerative clustering and prove that the symmetric KL divergence is an upper-bound for uni-modal Gaussian distributions. This leads to a very powerful yet elegant method for bottom-up agglomerative clustering with strong theoretical guarantees. We introduce albedo and reflectance fields as features for the distance computations. We compare against other established methods to bring out possible pros and cons of the proposed method.