当前位置: X-MOL 学术J. Appl. Stat. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2020-06-11 , DOI: 10.1080/02664763.2020.1779194
Soyoung Park 1 , Alicia Carriquiry 1
Affiliation  

We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr.



中文翻译:

使用边缘像素量化 2D 图像的相似性:鞋类印象取证比较的应用

我们提出了一种新方法来量化来自未知来源的印象 ( Q ) 和来自已知来源的测试印象 ( K ) 之间的相似性。利用印象中几何同余的性质,使用图论和最大团(MC)的思想来量化对应程度。该算法使用图像中边缘的xy坐标作为数据。我们关注Q中的局部区域和K中的相应区域并提取特征进行比较。使用具有已知来源的图像对,我们训练一个随机森林来将图像对分为配对和非配对。我们收集了 60 双相同品牌和型号的鞋子的印象,这些鞋子穿了 6 个月。使用一组不同的非常相似的鞋子,我们根据将图像正确分类到源类的准确性来评估算法的性能。使用分类错误率和 ROC 曲线,我们将所提出的方法与文献中的其他算法进行比较,并表明对于这些数据,我们的方法相对于其他方法显示出良好的分类性能。该算法可以使用 R 包shoeprintr实现。

更新日期:2020-06-11
down
wechat
bug