Locally Linear Embedding based on Image Euclidean Distance

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.

关键词： Locally Linear Embedding Image Euclidean Distance face detection

作者: Lijing Zhang Ning Wang

作者单位: Network Administration Center North China Electric Power University Baoding, 071003, Hebei province, Department of Computer Science North China Electric Power University Baoding, 071003, Hebei province

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）