核鉴别分析在图像集合匹配中的应用

Kernel Discriminant Analysis and Its Application in Image Set Matching

图像集匹配是当前图像处理和模式识别领域研究的热点问题之一。处理图像集合匹配一般将其映射到高维流形,然后在流形上度量2个点之间的距离。本文使用协方差矩阵对图像集合建模,把图像集合表达为黎曼流形上的一个点,将图像集的匹配问题转化为黎曼流形上的点的匹配问题,最后应用核鉴别分析方法进行分类。在基于图像集合的对象识别应用中测试本文所提出的算法,在公开数据库上的实验结果表明,本文所提出的方法在识别率上超越了当前主流的图像集匹配算法。

Image set matching attracts increasing attention in the field of pattern recognition. A convenient way of dealing with image sets is to represent them as points on manifolds. We naturally formulate the problem of image set matching as matching points lying on the Riemannian manifold spanned by covariance matrices. We derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to a Euclidean space. With the explicit mapping,a kernel version of linear discriminant analysis is applied to classify the image sets. The proposed method is evaluated on set-based object classification tasks.Extensive experimental results show that the proposed method outperforms other state of the art set-based matching methods in the public database.