Zhu Lei, Shen Jialie, Xie Liang, Cheng Zhiyong
IEEE Trans Cybern. 2017 Nov;47(11):3941-3954. doi: 10.1109/TCYB.2016.2591068. Epub 2016 Oct 21.
Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by exploiting auxiliary texts around images. In our method, relations between images and semantic topics are first discovered via robust collective non-negative matrix factorization. Afterwards, a unified topic hypergraph, where images and topics are represented with independent vertices and hyperedges, respectively, is constructed to model inherent high-order semantic correlations of images. Finally, hashing codes and functions are learned by simultaneously enforcing semantic consistence and preserving the discovered semantic relations. Experiments on publicly available datasets demonstrate that THH can achieve superior performance compared with several state-of-the-art methods, and it is more suitable for mobile image retrieval.
哈希将高维特征压缩为紧凑的二进制代码。由于其低数据传输成本和快速检索响应，它是支持高效移动图像检索的有前途的技术之一。然而，现有的大多数哈希策略仅仅依赖于低级特征。因此，它们可能生成具有有限判别能力的哈希码。此外，它们中的许多未能利用图像之间固有的复杂高阶语义相关性。受这些观察结果的启发，我们提出了一种新颖的无监督哈希方案，称为主题超图哈希（THH），以解决这些局限性。THH通过利用图像周围的辅助文本有效地减轻了哈希码的语义不足。在我们的方法中，首先通过鲁棒的集体非负矩阵分解发现图像与语义主题之间的关系。然后，构建一个统一的主题超图，其中图像和主题分别由独立的顶点和超边表示，以对图像固有的高阶语义相关性进行建模。最后，通过同时强制语义一致性和保留发现的语义关系来学习哈希码和函数。在公开可用数据集上的实验表明，与几种最新方法相比，THH可以实现卓越的性能，并且它更适合于移动图像检索。
