Hashing has been widely investigated for large-scale image retrieval due to its search effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided Hashing method coupled with binary matrix factorization to perform more effective nearest neighbor image search by simultaneously exploring the weakly-supervised rich community-contributed information and the underlying data structures. To uncover the underlying semantic information from the weakly-supervised user-provided tags, the binary matrix factorization model is leveraged for learning the binary features of images while the problem of imperfect tags is well addressed. The uncovered semantic information enables to well guide the discrete hash code learning. The underlying data structures are discovered by adaptively learning a discriminative data graph, which makes the learned hash codes preserve the meaningful neighbors. To the best of our knowledge, the proposed method is the first work that incorporates the hash code learning, the semantic information mining and the data structure discovering into one unified framework. Besides, the proposed method is extended to one deep approach for the optimal compatibility of discriminative feature learning and hash code learning. Experiments are conducted on two widely-used social image datasets and the proposed method achieves encouraging performance compared with the state-of-the-art hashing methods.

中文翻译：

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用于社交图像检索的弱监督语义引导哈希

由于其搜索效率和计算效率，散列已被广泛研究用于大规模图像检索。在这项工作中，我们提出了一种新颖的语义引导散列方法，结合二元矩阵分解，通过同时探索弱监督的丰富社区贡献信息和底层数据结构来执行更有效的最近邻图像搜索。为了从弱监督的用户提供的标签中揭示潜在的语义信息，利用二元矩阵分解模型来学习图像的二元特征，同时很好地解决了不完美标签的问题。未覆盖的语义信息能够很好地指导离散哈希码学习。通过自适应学习判别数据图来发现底层数据结构，这使得学习的哈希码保留有意义的邻居。据我们所知，所提出的方法是第一个将哈希码学习、语义信息挖掘和数据结构发现整合到一个统一框架中的工作。此外，所提出的方法被扩展为一种深度方法，以实现判别特征学习和哈希码学习的最佳兼容性。在两个广泛使用的社交图像数据集上进行了实验，与最先进的散列方法相比，所提出的方法取得了令人鼓舞的性能。语义信息挖掘和数据结构发现合二为一。此外，所提出的方法被扩展为一种深度方法，以实现判别特征学习和哈希码学习的最佳兼容性。在两个广泛使用的社交图像数据集上进行了实验，与最先进的散列方法相比，所提出的方法取得了令人鼓舞的性能。语义信息挖掘和数据结构发现合二为一。此外，所提出的方法被扩展为一种深度方法，以实现判别特征学习和哈希码学习的最佳兼容性。在两个广泛使用的社交图像数据集上进行了实验，与最先进的散列方法相比，所提出的方法取得了令人鼓舞的性能。