Robust multiple model estimation with Jensen-Shannon Divergence

In order to estimate multiple structures without prior knowledge of the noise scale, this paper utilizes Jensen-Shannon Divergence (JSD), which is a similarity measurement method, to represent the relations between pairwise data conceptually. This conceptual representation encompasses the geometrical relations between pairwise data as well as the information about whether pairwise data coexist in one model's inlier set or not. Tests on datasets comprised of noisy inlier and a large percentage of outliers demonstrate that the proposed solution can efficiently estimate multiple models without prior information. Superior performance in terms of synthetic experiments and pragmatic tests is also demonstrated to validate the proposed approach.