Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity graph has been extensively studied for large-scale image retrieval. However, most graph-based hashing methods resort to intractable binary quadratic programs, making them unscalable to massive data. In this paper, we propose a novel graph convolutional network-based hashing framework, dubbed GCNH, which directly carries out spectral convolution operations on both an image set and an affinity graph built over the set, naturally yielding similarity-preserving binary embedding. GCNH fundamentally differs from conventional graph hashing methods which adopt an affinity graph as the only learning guidance in an objective function to pursue the binary embedding. As the core ingredient of GCNH, we introduce an intuitive asymmetric graph convolutional (AGC) layer to simultaneously convolve the anchor graph, input data, and convolutional filters. By virtue of the AGC layer, GCNH well addresses the issues of scalability and out-of-sample extension when leveraging affinity graphs for hashing. As a use case of our GCNH, we particularly study the semisupervised hashing scenario in this paper. Comprehensive image retrieval evaluations on the CIFAR-10, NUS-WIDE, and ImageNet datasets demonstrate the consistent advantages of GCNH over the state-of-the-art methods given limited labeled data.

中文翻译：

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图卷积网络散列

近来，已经广泛研究了通过亲和图学习保留相似性的二进制代码的基于图的哈希，以用于大规模图像检索。但是，大多数基于图的散列方法求助于难解的二进制二次程序，从而使其无法扩展至海量数​​据。在本文中，我们提出了一种新颖的基于图卷积网络的哈希框架，称为GCNH，它可以直接对图像集和建立在该图像集上的亲和图执行频谱卷积运算，自然会产生相似度保持二进制。GCNH从根本上不同于传统的图哈希方法，后者采用亲和图作为目标函数中追求二进制嵌入的唯一学习指导。作为GCNH的核心成分，我们引入了直观的非对称图卷积（AGC）层，以同时对锚定图，输入数据和卷积过滤器进行卷积。借助AGC层，当利用亲和图进行哈希处理时，GCNH很好地解决了可伸缩性和样本外扩展的问题。作为GCNH的用例，我们特别研究了本文中的半监督哈希方案。在有限的标签数据下，对CIFAR-10，NUS-WIDE和ImageNet数据集的全面图像检索评估表明，GCNH相对于最新方法具有一致的优势。我们在本文中特别研究了半监督哈希方案。在有限的标签数据下，对CIFAR-10，NUS-WIDE和ImageNet数据集的全面图像检索评估表明，GCNH相对于最新方法具有一致的优势。我们在本文中特别研究了半监督哈希方案。在有限的标签数据下，对CIFAR-10，NUS-WIDE和ImageNet数据集的全面图像检索评估表明，GCNH相对于最新方法具有一致的优势。