On the combination of two visual cognition systems using combinatorial fusion

When combining decisions made by two separate visual cognition systems, statistical means such as simple average (M (1)) and weighted average (M (2) and M (3)), incorporating the confidence level of each of these systems have been used. Although combination using these means can improve each of the individual systems, it is not known when and why this can happen. By extending a visual cognition system to become a scoring system based on each of the statistical means M (1), M (2), and M (3) respectively, the problem of combining visual cognition systems is transformed to the problem of combining multiple scoring systems. In this paper, we examine the combined results in terms of performance and diversity using combinatorial fusion, and study the issue of when and why a combined system can be better than individual systems. A data set from an experiment with twelve trials is analyzed. The findings demonstrated that combination of two visual cognition systems, based on weighted means M (2) or M (3), can improve each of the individual systems only when both of them have relatively good performance and they are diverse.