Consensus maximisation using influences of monotone boolean functions
Abstract
Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Function (MBF) defined over the Boolean Cube. Then, we link the concept of influences (in a MBF) to the concept of outlier (in MaxCon) and show that influences of points belonging to the largest structure in data would generally be smaller under certain conditions. Based on this observation, we present an iterative algorithm to perform consensus maximisation. Results for both synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly. This is particularly important where there are large number of outliers (gross or pseudo) in the observed data.
Document Type
Conference Proceeding
Date of Publication
2021
Publication Title
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
School
School of Science
RAS ID
36863
Funders
Australian Research Council
Grant Number
ARC Numbers : LP190100165, DP200103448, DP200101675
Grant Link
http://purl.org/au-research/grants/arc/LP190100165
Copyright
free_to_read
Comments
Tennakoon, R., Suter, D., Zhang, E., Chin, T. J., Bab-Hadiashar, A. (2021). Consensus maximisation using influences of monotone boolean functions. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2865-2874). IEEE.
https://doi.org/10.1109/CVPR46437.2021.00289
An open access version of this paper is provided by the Computer Vision Foundation.