Abstract In this paper, we introduce an approach to discriminate unconventional images and their intelligent filtering. As the target data to this issue are huge and consequently, a handling approach might potentially be a very time consuming one, one of the major challenges to be solved by this introduced approach is its ability for dealing with large-scale datasets. A deep neural network might be a good option to resolve this challenge. It can provide a good accuracy while dealing with huge databases. In the proposed approach, the new architecture is introduced using a combination of AlexNet and LeNet architectures. It uses convolutional, polling and fully-connected layers. The results are tested on two large-scale datasets. These tests show that the introduced architecture is more accurate than the other recently developed methods in identifying unconventional images. The proposed approach may be used in different applications such as intelligent filtering of unconventional images or medical images analysis.

中文翻译：

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传统深度神经网络学习的深度特征

摘要 在本文中，我们介绍了一种区分非常规图像及其智能过滤的方法。由于这个问题的目标数据很大，因此处理方法可能非常耗时，这种引入的方法要解决的主要挑战之一是其处理大规模数据集的能力。深度神经网络可能是解决这一挑战的不错选择。它可以在处理庞大的数据库时提供良好的准确性。在所提出的方法中，使用 AlexNet 和 LeNet 架构的组合引入了新架构。它使用卷积、轮询和全连接层。结果在两个大规模数据集上进行了测试。这些测试表明，引入的架构在识别非常规图像方面比其他最近开发的方法更准确。所提出的方法可用于不同的应用，例如非常规图像的智能过滤或医学图像分析。