Oceanographers rely on advanced digital imaging systems to assess the health of marine ecosystems. The majority of the imagery collected by these systems do not get annotated due to lack of resources. Consequently, the expert labeled data is not enough to train dedicated deep networks. Meanwhile, in the deep learning community, much focus is on how to use pre-trained deep networks to classify out-of-domain images and transfer learning. In this paper, we leverage these advances to evaluate how well features extracted from deep neural networks transfer to underwater image classification. We propose new image features (called ResFeats) extracted from the different convolutional layers of a deep residual network pre-trained on ImageNet. We further combine the ResFeats extracted from different layers to obtain compact and powerful deep features. Moreover, we show that ResFeats consistently perform better than their CNN counterparts. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on MLC, Benthoz15, EILAT and RSMAS datasets.

海洋学家依靠先进的数字成像系统评估海洋生态系统的健康状况。由于缺乏资源，这些系统收集的大多数图像都没有注释。因此，专家标记的数据不足以训练专用的深度网络。同时，在深度学习社区中，很多焦点都集中在如何使用预训练的深度网络对域外图像进行分类和转移学习上。在本文中，我们利用这些进展来评估从深度神经网络提取的特征如何转移到水下图像分类中。我们提出了从ImageNet上预先训练的深度残差网络的不同卷积层提取的新图像特征（称为ResFeats）。我们进一步结合了从不同图层提取的ResFeats，以获得紧凑而强大的深层特征。此外，我们显示ResFeats始终比CNN同行表现更好。提供的实验结果显示了具有最先进分类准确性的ResFeats在MLC，Benthoz15，EILAT和RSMAS数据集上的有效性。