An adaptive image Euclidean distance
An adaptive image Euclidean distance
复制标题
自适应图像欧几里德距离
DOI：
10.1016/j.patcog.2008.07.017
复制
发表时间：
2009-03-01
影响因子：
8
通讯作者：
Lu, Bao-Liang
中科院分区：
文献类型：
--
作者：
Li, Jing;Lu, Bao-Liang
图像欧几里德距离（IMED）考虑了不同图像像素之间的空间关系，可以很容易地嵌入到现有的基于欧几里德距离的图像识别算法中。IMED利用相邻像素灰度值差异小的先验知识，根据像素间的空间距离定义度量矩阵。本文提出了一种既考虑图像的先验空间知识，又考虑图像的先验灰度知识的自适应图像欧几里得距离算法。与IMED相比，该方法最大的优点是使度量矩阵自适应于相关图像的内容。提出了两种利用灰度信息的方法。一种是基于灰度距离，另一种是基于灰度的余弦不相似度。在两个人脸数据库和一个手写数字数据库上进行的实验表明，当嵌入最近邻分类器、主成分分析和支持向量机时，该方法的分类准确率最高。(C) 2008 Elsevier Ltd版权所有。
The image Euclidean distance (IMED) considers the spatial relationship between the pixels of different images and can easily be embedded in existing image recognition algorithms that are based on Euclidean distance. IMED uses the prior knowledge that pixels located near one another have little variance in gray scale values, and defines a metric matrix according to the spatial distance between pixels. In this paper, we propose an adaptive image Euclidean distance (AIMED), which considers not only the prior spatial knowledge, but also the prior gray level knowledge from images. The most important advantage of the proposed AIMED over IMED is that AIMED makes the metric matrix adaptive to the content of the concerned images. Two ways of using gray level information are proposed. One is based on gray level distances, and the other is based on cosine dissimilarity of gray levels. Experiments on two facial databases and a handwritten digital database show that AIMED achieves the highest classification accuracy when it is embedded in nearest neighbor classifiers, principal component analysis, and support vector machines. (C) 2008 Elsevier Ltd. All rights reserved.