Multi-model Estimation Based on Jaccard Distance and Conceptual Clustering
YU Yong-yan · Jisuanji gongcheng · 2012
Multi-RANSAC and RHT these methods are incapable to solve multi-models estimation effectually,and a multi-model estimation method with model-based clustering in conceptual space is proposed.Each data point is represented with a preference set of hypotheses models preferred by that point,and the Jaccard distance between two preference sets is described as a attribute of an data point,to perform a clustering operation using the improved Cobweb algorithm based on the attribute of the data points.Neither this method requires prior specification of the number of models,nor it necessitates parameters transformation,so that it can overcome missing detection and false detection of crossing models.Experimental results show the obvious effect and greater accuracy of the algorithm,thus can be used widely by vanishing point detection,self-calibration of camera,etc.