KL Divergence Based Agglomerative Clustering for Automated Vitiligo Grading

Mithun Das Gupta, Srinidhi Srinivasa, Madhukara J, Meryl Antony

Abstract:

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 bottomup 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.