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l2,p-norm sequential bilateral 2DPCA: a novel robust technology for underwater image classification and representation

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Abstract

Many recently proposed robust two-dimensional principal component analysis (2DPCA) approaches can suppress the sensitivity to outliers in images to some extent. However, most approaches can neither perfectly minimize the reconstruction error nor use fewer coefficients to conveniently represent image information. To alleviate these deficiencies, we developed a novel robust 2DPCA approach for underwater image analysis, called l2,p-sequential bilateral-2DPCA (l2,p-SB-2DPCA). The outstanding advantages of l2,p-SB-2DPCA are as follows. First, our model uses the l2,p-norm as the metric criterion of the objective function, which not only improves the robustness of the algorithm but also preserves the basic properties of 2DPCA. Second, we establish the relationship between the variance of the projection data and the corresponding input data in both the row and column directions, which makes the model achieve good recognition ability while using fewer coefficients and further improves the interpretability of the model. Finally, to obtain the optimal value of l2,p-SB-2DPCA, we present an iterative algorithm. The experimental results show that the proposed algorithm achieves the best performance on three underwater datasets and one extended face dataset compared with other robust 2DPCA approaches.

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Acknowledgments

This work is supported by the Best Sea Assembly and the Control Technology Institute. The authors would like to thank Jian Xu, Xue Du, Juan Li and Guangjia Chen for providing assistance with the underwater experiments. This work is also supported in part by the National Natural Science Foundation of China under Grants 51609046 and 51709062 and in part by Research Funds for the Underwater Vehicle Technology Key Laboratory of China under Grants 614221502061701 and 6142215180107.

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  1. College of Automation, Harbin Engineering University, Harbin, 150001, Heilongjiang, China

    Pengfei Bi, Jian Xu, Xue Du, Juan Li & Guangjia Chen

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  1. Pengfei Bi
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  2. Jian Xu
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  3. Xue Du
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  4. Juan Li
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  5. Guangjia Chen
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Correspondence to Jian Xu.

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Bi, P., Xu, J., Du, X. et al. l2,p-norm sequential bilateral 2DPCA: a novel robust technology for underwater image classification and representation. Neural Comput & Applic 32, 17027–17041 (2020). https://doi.org/10.1007/s00521-020-04936-1

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  • DOI: https://doi.org/10.1007/s00521-020-04936-1

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