• 图形图像 •

### 多种表示的图像分类方法

1. 1.哈尔滨理工大学 计算机科学与技术学院，哈尔滨 150080
2.北京兆芯集成电路有限公司，北京 100084
3.酒泉卫星发射中心，甘肃 敦煌 736200
• 出版日期:2019-12-01 发布日期:2019-12-10

### Multiple Representations for Image Classification Approaches

CHEN Deyun, FU Lijun, ZHANG Xuesong, YU Liang, CHEN Hailong, LI Ao

1. 1.School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
2.Beijing Zhaoxin Semiconductor Co., Ltd., Beijing 100084, China
3.Jiuquan Satellite Launch Center, Dunhuang, Gansu 736200, China
• Online:2019-12-01 Published:2019-12-10

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.