论文标题
确定与准欧几里得公制的图像相似性
Determining Image similarity with Quasi-Euclidean Metric
论文作者
论文摘要
图像相似性是图像分析中的核心概念，因为它在计算机视觉，图像处理和模式识别中的广泛应用。我们研究的目的是评估准欧几里得度量标准作为图像相似性度量，并分析其如何与SSIM和Euclidean Metric（例如SSIM和Euclidean Metric）相对的方式。在本文中，我们分析了我们自己的新手数据集中的两个图像之间的相似性，并评估了其针对欧几里得距离指标和SSIM的性能。我们还提出了实验结果，并表明我们提出的实施应用于新手数据集时，在有效性和准确性方面，我们提出的实施与标准指标相比提供了不同的结果。在某些情况下，我们的方法论预测了出色的性能，还有趣地注意到，我们的实施在认识到相似性方面是迈出的一步
Image similarity is a core concept in Image Analysis due to its extensive application in computer vision, image processing, and pattern recognition. The objective of our study is to evaluate Quasi-Euclidean metric as an image similarity measure and analyze how it fares against the existing standard ways like SSIM and Euclidean metric. In this paper, we analyzed the similarity between two images from our own novice dataset and assessed its performance against the Euclidean distance metric and SSIM. We also present experimental results along with evidence indicating that our proposed implementation when applied to our novice dataset, furnished different results than standard metrics in terms of effectiveness and accuracy. In some cases, our methodology projected remarkable performance and it is also interesting to note that our implementation proves to be a step ahead in recognizing similarity when compared to
