基于Log-Gabor滤波与黎曼流形学习的图像识别算法

Image Recognition Algorithm Based on Log-Gabor Wavelet and Riemannian Manifold Learning

在图像识别的研究中,黎曼流形学习不能有效消除图像中的冗余信息.基于上述原因,文中提出基于LogGabor滤波与黎曼流形学习的图像识别算法.首先使用Log-Gabor滤波器处理图像,获得维数较高的Log-Gabor图像特征,然后使用黎曼流形学习降维图像特征.研究表明,Log-Gabor滤波与黎曼流形学习的融合算法符合人类视觉感知的过程.文中算法对于光照、角度变化具有较好的鲁棒性,在多个标准数据库上的仿真实验验证文中算法的有效性.

In image recognition applications, Riemannian manifold learning algorithms can not eliminate the redundant information in images effectively. Therefore, an image recognition algorithm based on Log-Gabor wavelet and Riemannian manifold learning is presented. Firstly, images are processed by the Log-Gabor filter to obtain high-dimensional Log-Gabor image features. Then, the Riemannian manifold learning algorithm is used to reduce the dimensionality of the image features. Research shows that the integration of Log-Gabor wavelet and Riemannian manifold learning is in accord with the process of human visual perception. The proposed algorithm has better robustness to illumination and angle variation of the image. Experimental results on several standard databases indicate the effectiveness of the proposed algorithm.