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A Scene Image is Nonmutually Exclusive—A Fuzzy Qualitative Scene Understanding

机译:场景图像互斥互斥-模糊定性场景理解

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摘要

Ambiguity or uncertainty is a pervasive element of many real-world decision-making processes. Variation in decisions is a norm in this situation when the same problem is posed to different subjects. Psychological and metaphysical research has proven that decision making by humans is subjective. It is influenced by many factors such as experience, age, background, etc. Scene understanding is one of the computer vision problems that fall into this category. Conventional methods relax this problem by assuming that scene images are mutually exclusive; therefore, they focus on developing different approaches to perform the binary classification tasks. In this paper, we show that scene images are nonmutually exclusive and propose the fuzzy qualitative rank classifier (FQRC) to tackle the aforementioned problems. The proposed FQRC provides a ranking interpretation instead of binary decision. Evaluations in terms of qualitative and quantitative measurements using large numbers and challenging public scene datasets have shown the effectiveness of our proposed method in modeling the nonmutually exclusive scene images.
机译:模糊性或不确定性是许多现实世界决策过程中普遍存在的因素。在同一情况下,不同主体面临决策时,决策差异是一种规范。心理和形而上学研究已经证明,人类的决策是主观的。它受经验,年龄,背景等许多因素的影响。场景理解是属于此类的计算机视觉问题之一。常规方法通过假定场景图像互斥来缓解此问题。因此,他们专注于开发执行二进制分类任务的不同方法。在本文中,我们表明场景图像是互斥的,并提出了模糊定性等级分类器(FQRC)来解决上述问题。提出的FQ​​RC提供了排名解释,而不是二进制决策。使用大量和具有挑战性的公共场景数据集进行的定性和定量测量方面的评估表明,我们提出的方法在建模非互斥场景图像方面是有效的。

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