(2D)2k-NNDA: Two-directional two-dimensional k-nearest neighbour discriminant analysis for target recognition

摘要：

An image feature extraction technique, two-directional two-dimensional k-nearest neighbour discriminant analysis ((2D)2k-NNDA), is presented from the viewpoint of the k-nearest neighbour (k-NN) classification, which is an extension of 2DNNDA based the idea of the nearest neighbour (1-NN) classification. Similar to 2DNNDA, (2D)2k-NNDA makes use of the matrix representation of images and does not assume the class densities belong to any particular parametric family. (2D)2k-NNDA is applied to target recognition and the results demonstrate that (2D)2k-NNDA achieves at least the same or even higher recognition accuracy than the existing 2D subspace methods.

关键词： two-dimensional principal component analysis (2DPCA) two-dimensional linear discriminant analysis (2DLDA) two-dimensional nearest neighbour discriminant analysis (2DNNDA) target recognition

作者: L.P. Hu C. Wang H.C. Yin

作者单位: National Key Laboratory of Target and Environment Electromagnetic Scattering and Radiation, Beijing Institute of Environmental Characteristics, Beijing 100854, China

会议类型: 国际会议

会议名称: 2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

会议地点: 成都

会议语种:英文

页码: 1631-1634

在线出版日期: 2011-10-24（万方平台首次上网日期，不代表论文的发表时间）