Abstract
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: M1: simple average, M2: weighted average using σ, and M3: weighted average using σ2. 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. M1, M2, M3, and combinatorial fusion on M1 are computed. Our two main results are: (a) Six of the ten experiments show performance order M1 >; M2 >; M3 while the other four experiments exhibit the opposite order; (b) Combinatorial fusion based on M1 performs better than M1 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.