Abstract 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 proposedalgorithm.
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2015, Vol. 28 
