Combining multiple visual cognition systems for joint decision-making using combinatorial fusion

Cognitive decision-making based on visual sensory input has been a topic of intensive interest. When combining these visual cognition systems, three models of strategy have been proposed and widely used: M
₁
: simple average, M
₂
: weighted average using σ, and M
₃
: weighted average using σ
²
. In this paper, we extend each visual cognition system to a scoring system using combinatorial fusion analysis, a framework which has proven effective for the optimization of multiple evaluation methods in several other computational domains. Ten experiments on two visual systems each are conducted. M
₁
, M
₂
, M
₃
, and combinatorial fusion on M
₁
are computed. Our two main results are: (a) Six of the ten experiments show performance order M
₁
>; M
₂
>; M
₃
while the other four experiments exhibit the opposite order; (b) Combinatorial fusion based on M
₁
performs better than M
₁
in eight of the ten experiments. It is demonstrated that Combinatorial Fusion Analysis is useful in the study of visual cognition. Our results exhibit a new method to better analyze and make joint decisions in visual cognition using Combinatorial Fusion Analysis.