关键词: 图像分类, 二维主成分分析（2DPCA）, 快速傅里叶变换（FFT）, 稀疏方法, 多种表示方法

Abstract: Extracted salient features from different images are significant for image classification. However, single image classification methods are not robust for all the images. This paper extracts different features by multiple views and fuses them to effectively address the above problem. Firstly, this paper uses 2DPCA (two-dimensional principal component analysis) to extract features of images, which are utilized to reconstruct images (also referred to as virtual images). Secondly, it applies FFT (fast Fourier transform) to obtain spectrum features. Then, it exploits sparse method to get scores from original images, virtual images and spectrum features, respectively. Finally, this paper uses a novel fusion mechanism to integrate the obtained scores above, and classifies the images according to the newly acquired scores. The obtained multiple features are complementary with original images, which makes the proposed algorithm more robust. The proposed method is sparse, which improves the performance of image classification. In addition, it can automatically extract parameters rather than manual settings. Experimental results show that this algorithm has higher accuracy of image classification under different situations.

Key words: image classification, two dimension principal component analysis (2DPCA), fast Fourier transform (FFT), sparse method, multiple representation methods