论文标题
双层语义转移深度散列以进行有效的社交形象检索
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
论文作者
论文摘要
社交网络存储并传播大量用户共享图像。 Deep Hashing是一种有效的索引技术，可以支持大规模的社交图像检索，因为其深层表示能力，快速检索速度和较低的存储成本。特别是，无监督的深哈希具有良好的可扩展性，因为它不需要任何手动标记的数据进行培训。但是，由于缺乏标签指导，现有方法在优化大量深神经网络参数时会遭受严重的语义短缺。不同的是，在本文中，我们提出了一种双层语义转移深度哈希（DSTDH）方法，以通过统一的深层学习框架来减轻此问题。我们的模型目标是通过专门利用与社交图像相关的用户生成的标签来学习语义增强的深度哈希码。具体而言，我们设计了一个互补的双级语义传递机制，以有效地发现标签的潜在语义，并无缝将其传递到二进制哈希码中。一方面，实例级的语义直接从相关的标签中直接保存在哈希码中，并消除了不良噪声。此外，构建了图像概念超图，用于间接将图像和标签的潜在高阶语义相关性转移到哈希码中。此外，Hash代码是通过离散哈希优化策略同时获得深度表示学习的。与最先进的哈希方法相比，对两个公共社交图像检索数据集进行了广泛的实验，可以验证我们方法的出色性能。可以在https://github.com/research202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202020202A法术中获得的源代码
Social network stores and disseminates a tremendous amount of user shared images. Deep hashing is an efficient indexing technique to support large-scale social image retrieval, due to its deep representation capability, fast retrieval speed and low storage cost. Particularly, unsupervised deep hashing has well scalability as it does not require any manually labelled data for training. However, owing to the lacking of label guidance, existing methods suffer from severe semantic shortage when optimizing a large amount of deep neural network parameters. Differently, in this paper, we propose a Dual-level Semantic Transfer Deep Hashing (DSTDH) method to alleviate this problem with a unified deep hash learning framework. Our model targets at learning the semantically enhanced deep hash codes by specially exploiting the user-generated tags associated with the social images. Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes. On the one hand, instance-level semantics are directly preserved into hash codes from the associated tags with adverse noise removing. Besides, an image-concept hypergraph is constructed for indirectly transferring the latent high-order semantic correlations of images and tags into hash codes. Moreover, the hash codes are obtained simultaneously with the deep representation learning by the discrete hash optimization strategy. Extensive experiments on two public social image retrieval datasets validate the superior performance of our method compared with state-of-the-art hashing methods. The source codes of our method can be obtained at https://github.com/research2020-1/DSTDH
