Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. The novelty of our network design is that we constrain one hidden layer to directly output the binary codes. This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization. In addition, we propose to incorporate independence and balance properties in the direct and strict forms into the learning schemes. We also include a similarity preserving property in our objective functions. The resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. To tackle this difficulty, we propose to learn the networks with alternating optimization and careful relaxation. Furthermore, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture that jointly learns to extract visual features and produce binary hash codes. Experimental results for the benchmark datasets show that the proposed methods compare favorably or outperform the state of the art.

中文翻译：

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二进制深度神经网络的紧凑哈希码学习

使用深度神经网络学习用于图像检索问题的紧凑二进制代码最近引起了越来越多的关注。然而，由于哈希码的二进制约束，训练深度哈希网络具有挑战性。在本文中，我们提出了深度网络模型和学习算法，用于在无监督和有监督的方式下学习给定图像表示的二进制哈希码。我们网络设计的新颖之处在于我们限制一个隐藏层直接输出二进制代码。这种设计克服了之前一些工作中的一个具有挑战性的问题：由于二值化而优化非平滑目标函数。此外，我们建议以直接和严格的形式将独立性和平衡性纳入学习方案。我们还在我们的目标函数中包含了一个相似性保留属性。由此产生的涉及这些二元、独立和平衡约束的优化很难解决。为了解决这个困难，我们建议通过交替优化和仔细放松来学习网络。此外，通过利用卷积神经网络的强大能力，我们提出了一种端到端架构，可以共同学习提取视觉特征并生成二进制哈希码。基准数据集的实验结果表明，所提出的方法优于或优于现有技术。我们建议通过交替优化和仔细放松来学习网络。此外，通过利用卷积神经网络的强大能力，我们提出了一种端到端架构，可以共同学习提取视觉特征并生成二进制哈希码。基准数据集的实验结果表明，所提出的方法优于或优于现有技术。我们建议通过交替优化和仔细放松来学习网络。此外，通过利用卷积神经网络的强大能力，我们提出了一种端到端架构，可以共同学习提取视觉特征并生成二进制哈希码。基准数据集的实验结果表明，所提出的方法优于或优于现有技术。