The classification of images taken in special imaging environments except air is the first challenge in extending the applications of deep learning. We report on an UW-Net (Underwater Network), a new convolutional neural network (CNN) based network for underwater image classification. In this model, we simulate the visual correlation of background attention with image understanding for special environments, such as fog and underwater by constructing an inception-attention (I-A) module. The experimental results demonstrate that the proposed UW-Net achieves an accuracy of 99.3% on underwater image classification, which is significantly better than other image classification networks, such as AlexNet, InceptionV3, ResNet and Se-ResNet. Moreover, we demonstrate the proposed IA module can be used to boost the performance of the existing object recognition networks. By substituting the inception module with the I-A module, the Inception-ResnetV2 network achieves a 10.7% top1 error rate and a 0% top5 error rate on the subset of ILSVRC-2012, which further illustrates the function of the background attention in the image classifications. Underwater images and videos contain a lot of valuable information for many underwater scientific researches (Klausner & Azimi-Sadjadi, 2019; Peng et al., 2018) . However, the image analysis systems and classification algorithms designed for natural images (Redmon & Farhadi, 2018; He et al., 2017) cannot be directly applied to the underwater images due to the complex distortions existed in underwater images (e.g., low contrast, blurring, non-uniform brightness, non-uniform color casting and noises) and there is, to the best of our knowledge, no model for underwater image classification. Except for the inevitable distortions exhibited in underwater images, there are other three key problems for the classification of underwater images: (1) the background in underwater images taken in different environments are various; (2) the salient objects such as ruins, fish, diver exist not only in underwater environment, but also in air. The features extracted from the salient objects cannot be relied on primarily in the classification of underwater images; and (3) since the classification of underwater images is only a dualistic classification task, the structure of the designed network should be simple to avoid the over-fitting. Increasing the depth and width of a CNN can usually improve the performance of the model, but is more prone to cause over-fitting when the training dataset is limited, and needs more computational resource (LeCun et al., 2015; Srivastava et al., 2014) . To remit this issue, (Szegedy et al., 2015) proposed the inception module, which simultaneously performs the multi-scale convolution and pooling on a level of CNN to output multi-scale features. In addition, the attention mechanism (Chikkerur et al., 2010; Borji & Itti, 2012) is proposed and applied in the recent deep models which takes the advantage that human vision pays attention to different parts of the image depending on the recognition tasks Zhu et al., 2018; Ba et al., 2014) . Although these strategies play an important role in advancing the field of image classifications, we find that the large-scale features such as the background area play a more important role in the visual attention mechanism when people understanding of underwater images, which is unlike the attention mechanism applied in natural scene image classification (Xiao et al., 2015; Fu et al., 2017) . In this paper, we propose an underwater image classification network, called UW-Net. The overview network structure is shown in Fig. 1 . Unlike other models, the UW-Net pays more attention to the Figure 1 : The structure of the UW-Net. The bottom part is the output of the eighth layer in the I-A module. The red area represents a higher response of features for the underwater image classification. As shown, our I-A module concerns more about the background regions of underwater images. background features of images by construct the inception-attention (I-A) modules and thus achieves better performance. The contributions of this paper are as follows: (i) to the best of our knowledge, it is the first CNN-based model for underwater image classification; (ii) an inception-attention module is proposed, which joints the multi-dimensional inception module with the attention module to realize the multiple weighting of the output of various scales of features; (iii) this work is a first attempt to simulate the visual correlation between understanding images and background areas through I-A modules. The rest of the paper is organized as follows: Section 2 introduces the related work. The proposed UW-Net is described in Section 3. Section 4 illustrates the experimental results and analysis, and we summarize this paper in Section 5. A new underwater image classification network UW-Net is proposed in this work, wherein an inception-attention module is constructed. In this model, we simulate the visual correlation between understanding images and background areas through I-A modules, which joint the multidimensional inception module with the attention module to realize the multiple weighting of the output of various scales of features. The 100% accuracy on the training set and 99.3% accuracy on the testing set of the UW-Net is achieved benefiting from the refinement of the usefulness of multiscale features by the I-A module. In the future, we will try to improve the performance of other underwater image visual analysis models by introducing the proposed I-A module.
A visual understanding mechanism for special environment
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