Locally Linear Embedding based on Image Euclidean Distance

被引：1

作者：

Zhang, Lijing ^{[1
]}

Wang, Ning ^{[2
]}

机构：

[1] North China Elect Power Univ, Network Adm Ctr, Baoding 071003, Hebei Province, Peoples R China

[2] North China Elect Power Univ, Dept Comp Sci, Baoding 071003, Hebei Province, Peoples R China

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6 | 2007年

关键词：

Locally Linear Embedding; Image Euclidean distance; face detection;

D O I：

10.1109/ICAL.2007.4338886

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present an improved Locally Linear Embedding algorithm based on Image Euclidean distance (IMED) to replace the traditional Euclidean distance. IMED depending on pixel distance is robust to the noises in images. So in theory, applying the new distance metrics to LLE can bridge a gap, that is, traditional LLE is sensitive to noises. The improved algorithm highly enhances its stability to noises. We apply the algorithm to face detection, with SVM as the classifier, in the CBCL face database and test the detector on CMU frontal face test set. The result demonstrates a consistent performance improvement of the algorithms over the original version.

引用

页码：1914 / +

页数：2

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