title: The diversity rank-score function for combining human visual perception systems.
abstract: 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. This study involved several pairs of people, each viewing an event and reporting an observation, along with their confidence level. Each observer is treated as a visual perception system, and hence an associated scoring system is created based on the observer's confidence. A diversity rank-score function on a set of observation pairs is calculated using the notion of cognitive diversity between two scoring systems in the combinatorial fusion analysis framework. The resulting diversity rank-score function graph provides a powerful visualization tool for the diversity variation among a set of system pairs, helping to identify which system pairs are most likely to show improved performance with combination.
authors.affiliation: Division of Computer Science, Mathematics and Science, St. John's University, Queens, NY, USA. schweikc@stjohns.edu.
authors.forename: Christina
authors.initials: C
authors.lastname: Schweikert
authors.affiliation: Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.
authors.forename: Darius
authors.initials: D
authors.lastname: Mulia
authors.affiliation: Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.
authors.forename: Kilby
authors.initials: K
authors.lastname: Sanchez
authors.affiliation: Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.
authors.forename: D Frank
authors.initials: DF
authors.lastname: Hsu
keywords: Cognitive diversity; Combinatorial fusion analysis; Diversity rank-score function; Multiple scoring systems; Rank-score characteristic (RSC) function