10 April 2018 Low-contrast underwater living fish recognition using PCANet
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150Y (2018) https://doi.org/10.1117/12.2302695
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Quantitative and statistical analysis of ocean creatures is critical to ecological and environmental studies. And living fish recognition is one of the most essential requirements for fishery industry. However, light attenuation and scattering phenomenon are present in the underwater environment, which makes underwater images low-contrast and blurry. This paper tries to design a robust framework for accurate fish recognition. The framework introduces a two stage PCA Network to extract abstract features from fish images. On a real-world fish recognition dataset, we use a linear SVM classifier and set penalty coefficients to conquer data unbalanced issue. Feature visualization results show that our method can avoid the feature distortion in boundary regions of underwater image. Experiments results show that the PCA Network can extract discriminate features and achieve promising recognition accuracy. The framework improves the recognition accuracy of underwater living fishes and can be easily applied to marine fishery industry.
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Xin Sun, Xin Sun, Jianping Yang, Jianping Yang, Changgang Wang, Changgang Wang, Junyu Dong, Junyu Dong, Xinhua Wang, Xinhua Wang, } "Low-contrast underwater living fish recognition using PCANet", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150Y (10 April 2018); doi: 10.1117/12.2302695; https://doi.org/10.1117/12.2302695
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