Determining Image similarity with Quasi-Euclidean Metric
This addresses image similarity analysis for computer vision applications, but appears incremental as it tests a known metric on a new dataset.
The study evaluated the Quasi-Euclidean metric as an image similarity measure, comparing it against standard metrics like SSIM and Euclidean distance on a novice dataset, and found it sometimes outperformed them in effectiveness and accuracy.
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