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Effect of image resolution on intensity based scene illumination classification using neural network

机译:图像分辨率对神经网络基于强度的场景照明分类的影响

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摘要

In this paper, a framework for testing scene illumination classification with different image resolutions is proposed. The testing aims to provide the researchers with valuable information about the effect of image resolution on scene illumination classification using a neural network. The experiment is done by extracting three types of features from the images. These three types consist of statistical features, physic based features and histogram based features. It has been demonstrated that scene illumination classification can be affected by changing the image resolution. Despite the popular belief that high resolution images lead to better results, scene illumination classification by the proposed method performed best using low resolution images. At the second part of discussion, the reason behind this phenomenon is mathematically analysed and explained.
机译:本文提出了一种用于测试具有不同图像分辨率的场景照明分类的框架。该测试旨在使用神经网络为研究人员提供有关图像分辨率对场景照明分类的影响的有价值的信息。通过从图像中提取三种类型的特征来完成实验。这三种类型包括统计特征,基于物理的特征和基于直方图的特征。已经证明,可以通过改变图像分辨率来影响场景照明分类。尽管人们普遍认为高分辨率图像会带来更好的结果,但是使用低分辨率图像通过提出的方法进行场景照明分类效果最佳。在讨论的第二部分,对这一现象背后的原因进行了数学分析和解释。

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