Multi-label hashing is a new research topic in image retrieval. As images are usually associated with multiple semantic labels, there is multi-level semantic similarity such as very similar, normally similar and dissimilar among multi-label images. In order to obtain the multi-level semantic similarity, this letter constructs a hypergraph in label space by creating a hyperedge for each semantic label and including all images annotated with a common label into one hyperedge. In this way, the number of common hyperedges shared by the vertices in hypergraph can be used to encode the high-order semantic relations among multiple images. Considering the useful similarity information hidden in the instance space, a $k$NN graph in instance space is further constructed. By learning from both the hypergraph and $k$NN graph with spectral learning strategy, a graph regularized deep discrete hashing is developed which updates graph regularized binary codes and deep neural network based robust features iteratively in a discrete optimization framework. The results in comparison with nine state-of-the-art hashing methods on two multi-label image datasets such as MIRFLICKR-25 K and NUS-WISE demonstrate its effectiveness.

中文翻译：

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用于多标签图像检索的图正则化深度离散散列

多标签哈希是图像检索中的一个新的研究课题。由于图像通常与多个语义标签相关联，因此多标签图像之间存在非常相似、通常相似和不相似等多级语义相似性。为了获得多级语义相似度，该字母通过为每个语义标签创建一个超边，并将所有标注有公共标签的图像包含在一个超边中，从而在标签空间中构造一个超图。这样，超图中顶点共享的公共超边的数量就可以用来编码多幅图像之间的高阶语义关系。考虑到隐藏在实例空间中的有用的相似性信息，一个$千$进一步构建实例空间中的 NN 图。通过从超图和$千$具有谱学习策略的 NN 图，开发了一种图正则化深度离散散列，它在离散优化框架中迭代更新图正则化二进制代码和基于深度神经网络的鲁棒特征。与 MIRFLICKR-25 K 和 NUS-WISE 等两个多标签图像数据集上的九种最先进的散列方法相比，结果证明了其有效性。