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首页> 外文期刊>Arabian Journal for Science and Engineering >FFT Consolidated Sparse and Collaborative Representation for Image Classification
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FFT Consolidated Sparse and Collaborative Representation for Image Classification

机译：用于图像分类的FFT合并稀疏和协作表示

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摘要

Spectrum analysis can quickly extract and analyze frequency domain features of signal, and it has been widely applied in fields of image processing, noise processing and signal processing. Fast Fourier transform (FFT) is fast and efficient, because it can efficiently decrease complexity of discrete Fourier transform. As a consequence, FFT is a very good method for image frequency spectrum analysis. In this paper, we propose to consolidate frequency domain representation by sparse representation (SR) and collaborative representation classification (CRC) which has excellent performance in comparison with general sparse representation-associated classification algorithms. Our proposed novel method has three main phases. The first phase utilizes FFT to extract frequency domain features of original images, which are complementary with representations of the original images. The second phase of our proposed novel method exploits CRC or SR to obtain scores of original images and obtained features, respectively. The third phase integrates the scores of original images and obtained features and uses them to classify images. The major contribution of the proposed method is that it is usually more robust than methods using only FFT, CRC or state-of-art method CIRLRC for image classification. The experiments of image classification demonstrate that the simultaneous use of FFT and CRC or sparse representation classification has high accuracy on image recognition.
机译：频谱分析可以快速提取和分析信号的频域特征，已广泛应用于图像处理，噪声处理和信号处理领域。快速傅立叶变换（FFT）快速高效，因为它可以有效降低离散傅立叶变换的复杂度。因此，FFT是一种很好的图像频谱分析方法。在本文中，我们提出通过稀疏表示（SR）和协作表示分类（CRC）来合并频域表示，与一般的稀疏表示相关分类算法相比，该算法具有出色的性能。我们提出的新颖方法具有三个主要阶段。第一阶段利用FFT提取原始图像的频域特征，该特征与原始图像的表示互补。我们提出的新方法的第二阶段利用CRC或SR分别获得原始图像的分数和获得的特征。第三阶段整合原始图像的分数和获得的特征，并使用它们对图像进行分类。所提出的方法的主要贡献在于，它通常比仅使用FFT，CRC或最新方法CIRLRC进行图像分类的方法更鲁棒。图像分类实验表明，同时使用FFT和CRC或稀疏表示分类对图像识别具有很高的准确性。

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