FFT协同特征空间的图像表示方法

FFT Consolidated Feature Space Methods for Image Representation

快速傅里叶变换(FFT)方法已经在图像识别中有广泛的应用。但是,FFT方法面临一些挑战,比如:不同角度的遮挡、变化的光照和多变的面部表情等。将快速傅里叶变换和特征空间的图像表示方法融合起来解决上述问题。有以下阶段:①使用FFT从原始图像中提取频谱特征。②利用高斯核方法在特征空间中获得新的特征。新的特征和原始图像的训练样本分别使用稀疏表示来获得稀疏解。新的特征和原始图像的测试样本可以使用上述稀疏解及其训练样本来分别计算得分。随后,可以利用得分和新得分进行图像分类。这一方法在图像分类上具有稀疏性和鲁棒性,非常容易实现。实验结果表明,本文提出的方法在图像分类上具有高的准确率。

Fast Fourier Transform(FFT)method has been widely used in image recognition.However,FFT method faces some challenges,such as occlusion of different angles,varying lighting and changeable facial expression.We combine fast Fourier transform and feature space image representation method to solve the above problems.There are the following stages:①Use FFT to extract spectral features from the original image.②Use Gaussian kernel method to obtain new features in the feature space.The new features and the training samples of the original image use sparse representations to obtain sparse solutions.The new features and the test samples of original image can use the aforementioned sparse solution and its training samples to calculate scores respectively.Subsequently,the score and the new score can be used for image classification.This method has sparseness and robustness in image classification,and is very easy to implement.Experimental results show that the method proposed in this paper has high accuracy in image classification.