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Low-contrast Underwater Living Fish Recognition Using PCANet

机译：使用PCANet的低对比度水下活鱼识别

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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.
机译：海洋生物的定量和统计分析对于生态和环境研究至关重要。活鱼的识别是渔业的最基本要求之一。然而，在水下环境中存在光衰减和散射现象，这使得水下图像低对比度和模糊。本文试图设计一个可靠的框架来进行精确的鱼类识别。该框架引入了一个分为两个阶段的PCA网络，以从鱼类图像中提取抽象特征。在现实世界中的鱼类识别数据集上，我们使用线性SVM分类器并设置惩罚系数来征服数据不平衡的问题。特征可视化结果表明，该方法可以避免水下图像边界区域的特征失真。实验结果表明，PCA网络可以提取特征并获得有希望的识别精度。该框架提高了水下活鱼的识别精度，可以轻松地应用于海洋渔业。

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