基于Log-Gabor滤波特征的黎曼流形图像集分类算法*

生物神经中的感知理论符合黎曼流形,相比其它滤波器,Log-Gabor滤波器更适合人眼的非线性对数特性,因此两者结合符合人类视觉的感知过程.基于上述情况,文中利用协方差鉴别学习,提出基于Log-Gabor 滤波特征的黎曼流形图像集分类算法.使用Log-Gabor滤波器滤波图像,获得多尺度多方向的图像特征,然后对高维的协方差矩阵使用双向二维主成分分析进行降维,利用协方差鉴别学习进行分类.在多个标准数据库上的实验结果表明文中算法效果较好,从而验证算法的有效性.

Riemannian Manifold Image Set Classification Algorithm Based on Log-Gabor Wavelet Features

The perception theory of biological neurology coincides with Riemannian manifold, and Log-Gabor filter is more suitable for nonlinear human eye logarithmic characteristic than other filters.Therefore,the combination of Log-Gabor wavelet and Riemannian manifold accords with the process of human visual perception. Grounded on covariance discriminative learning(CDL), the Riemannian manifold image set classification algorithm based on Log-Gabor Wavelet features is presented.Each image is processed by Log-Gabor filter to get its multi-scale and multi-direction features. The two-directional two-dimensional principal component analysis is adopted to reduce the dimension of covariance matrix and then the covariance discriminative learning algorithm is applied for classification.The experimental results of the proposed algorithm on several standard datasets show the superiority of the algorithm in accuracy over state-of-the-art algorithms.