基于改进ISOMAP算法的图像分类

利用基于邻域的图像欧氏距离寻找最近邻,并用直接线性判别分析方法(Direct LDA)取代多维尺度分析法(MDS),提出一种改进的等距特征映射(ISOMAP)算法(KIMD-ISOMAP)进行降维.人脸图像分类试验表明:KIMD-ISOMAP提高了ISOMAP的分类能力,扩展了邻域半径的选取范围,在加高斯噪声和几何形变的情况下,该算法与其他方法相比,表现出较强的鲁棒性.

Image Classification Using Modified ISOMAP Method

The classical ISOMAP（isometric feature mapping，ISOMAP） method developed on reconstruction principle may not be optimal from the classification viewpoint. Besides，it is prone to suffer from the noise and the range of the neighborhood. In order to resolve these problems, a novel method called KIMD ISOMAP for dimensionality reduction was presented. Firstly, a modified image euclidean distance is proposed and used to find the suitable neighborhood. Then, direct linear discriminant analysis (Direct LDA) is used to replace multi dimensional scaling (MDS). Compared with ISOMAP, the experiments on face recognition show that KIMD ISOMAP enhances the ability of classification and extends the range of the neighborhood. In addition, the KIMD ISOMAP obtains a better performance than other algorithms for images classification with small noise and geometrical deformation.