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Robust deflated canonical correlation analysis via feature factoring for multi-view image classification

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Abstract

Canonical Correlation Analysis (CCA) and its kernel versions (KCCA) are well-known techniques adopted in feature representation and classification for images. However, their performances are significantly affected when the images are noisy and in multiple views. In this paper, the method of robust deflated canonical correlation analysis via feature factoring for multi-view image classification is proposed. In this method, a feature factoring matrix is introduced to measure proximities between each feature vector in the dimension and projection vector, through this we evaluate the contribution of each feature to the whole feature space. Therefore, we can assign specific weights to different features accordingly to suppress the noisy data. As images are captured in multi-view usually, we also propose a deflated CCA method to build multiple factoring matrices with respect to multiple projection vectors. In this way, we weigh the degree of importance of each feature in each projection respectively to get a better feature representation for multi-view images. Experimental results on several datasets such as ORL, COIL and USPS, demonstrate that our method can improve classification performance compared to other state-of-the-art CCA methods.

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Notes

  1. https://github.com/adamhui/deflateKcca

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Acknowledgements

This research was supported in part by National Natural Science Foundation of China (Grant No. 61672268).

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China

    Kai-fa Hui, Ernest Domanaanmwi Ganaa, Yong-zhao Zhan & Xiang-jun Shen

  2. Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications, Zhenjiang, Jiangsu, China

    Yong-zhao Zhan

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  1. Kai-fa Hui
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  2. Ernest Domanaanmwi Ganaa
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  3. Yong-zhao Zhan
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  4. Xiang-jun Shen
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Correspondence to Yong-zhao Zhan.

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Hui, Kf., Ganaa, E.D., Zhan, Yz. et al. Robust deflated canonical correlation analysis via feature factoring for multi-view image classification. Multimed Tools Appl 80, 24843–24865 (2021). https://doi.org/10.1007/s11042-021-10736-z

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