Fitting multiple models via density analysis in tanimoto space

Abstract

This paper deals with the extraction of multiple models from noisy, outlier-contaminated data. We build on the “preference trick” implemented by T-linkage, weakening the prior assumptions on the data: without requiring the tuning of the inlier threshold we develop a new automatic method which takes advantage of the geometric properties of Tanimoto space to bias the sampling toward promising models and exploits a density based analysis in the conceptual space in order to robustly estimate the models. Experimental validation proves that our method compares favourably with T-Linkage on public, real data-sets.