Fusion of two visual perception systems utilizing cognitive diversity

Elena Paolercio, Cameron McMunn-Coffran, Brian Mott, D. Hsu, Christina Schweikert
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引用次数: 7

Abstract

Decision-making tasks that are based on perception have been performed routinely by human beings in daily life and decision-makers in daily work. It has been observed that the combination of two perception-based decisions could be better than one, provided that the decision-makers communicate with each other and jointly adopt the most confident judgment; simply select the most confident judgment without any dyadic interaction, or communicate with each other on shareable arguments based on reasoning. In this paper, we report a recently conducted visual perception experiment with decisions from nineteen pairs of subjects and their reported confidence factors when making such a decision. Utilizing the concept of a “cognitive diversity” between two systems and the combinatorial fusion algorithm, our results demonstrated that the fusion of two visual perception decision systems can be better than each of the individual systems only if the two systems perform relatively good and they are cognitively diverse. Our study not only provides a robust algorithm to fuse two visual perception decision-making systems, but also suggests a resilient approach to use the cognitive diversity in fusion when the performance of each individual system is not known or cannot be obtained, which is often the case for complex problems.
利用认知多样性的两种视觉感知系统融合
基于感知的决策任务已经成为人类日常生活和决策者日常工作中的常规任务。观察到,如果决策者相互沟通并共同采取最自信的判断,则两个基于感知的决策组合可能优于一个决策;简单地选择最自信的判断,没有任何二元互动,或者基于推理的可共享论点进行交流。在本文中,我们报告了最近进行的一项视觉感知实验,其中包括19对受试者的决策以及他们在做出此类决策时报告的自信因素。利用两个系统之间的“认知多样性”概念和组合融合算法,我们的研究结果表明,只有当两个系统表现相对较好并且它们具有认知多样性时,两个视觉感知决策系统的融合才能比每个单独的系统更好。我们的研究不仅提供了一种鲁棒的算法来融合两个视觉感知决策系统,而且还提出了一种弹性方法,在每个单独系统的性能未知或无法获得时,在融合中使用认知多样性,这通常是复杂问题的情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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