Wang Liwei, Zhang Yan, Feng Jufu
Center for Information Sciences, School of Electronics Engineering and Computer Sciences, Peking University, Beijing, 100871, China.
IEEE Trans Pattern Anal Mach Intell. 2005 Aug;27(8):1334-9. doi: 10.1109/TPAMI.2005.165.
We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images. IMED is then applied to image recognition. The key advantage of this distance measure is that it can be embedded in most image classification techniques such as SVM, LDA, and PCA. The embedding is rather efficient by involving a transformation referred to as Standardizing Transform (ST). We show that ST is a transform domain smoothing. Using the Face Recognition Technology (FERET) database and two state-of-the-art face identification algorithms, we demonstrate a consistent performance improvement of the algorithms embedded with the new metric over their original versions.
我们提出了一种新的图像欧几里得距离，我们称之为图像欧几里得距离（IMED）。与传统的欧几里得距离不同，IMED考虑了像素的空间关系。因此，它对图像的小扰动具有鲁棒性。我们认为IMED是唯一直观合理的图像欧几里得距离。然后将IMED应用于图像识别。这种距离度量的关键优势在于它可以嵌入到大多数图像分类技术中，如支持向量机（SVM）、线性判别分析（LDA）和主成分分析（PCA）。通过涉及一种称为标准化变换（ST）的变换，嵌入相当高效。我们表明ST是一种变换域平滑。使用人脸识别技术（FERET）数据库和两种最先进的人脸识别算法，我们证明了嵌入新度量的算法相对于其原始版本在性能上有一致的提升。
