The diversity rank-score function for combining human visual perception systems.

There are many situations in which a joint decision, based on the observations or decisions of multiple individuals, is desired. The challenge is determining when a combined decision is better than each of the individual systems, along with choosing the best way to perform the combination. It has been shown that the diversity between systems plays a role in the performance of their fusion.

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 ) and weighted average (M and M ), 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 , M , and M respectively, the problem of combining visual cognition systems is transformed to the problem of combining multiple scoring systems.