Consensus Maximisation Using Influences of Monotone Boolean Functions
This addresses robust fitting in computer vision, particularly for handling large numbers of outliers, but is incremental as it builds on existing MaxCon methods.
The paper tackles the consensus maximisation problem for robust fitting in computer vision by linking it to monotone Boolean functions and using influences to identify outliers, resulting in an iterative algorithm that produces near-optimal solutions quickly, especially with many outliers.
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.