Efficient Weakly-supervised Discrete Hashing for Large-scale Social Image Retrieval
Efficient Weakly-supervised Discrete Hashing for Large-scale Social Image Retrieval
复制标题
用于大规模社交图像检索的高效弱监督离散哈希
DOI：
10.1016/j.patrec.2018.08.033
复制
发表时间：
2020
影响因子：
5.1
通讯作者：
Huaxiang Zhang
中科院分区：
文献类型：
--
作者：
Hui Cui;Lei Zhu;Chaoran Cui;Xiushan Nie;Huaxiang Zhang
散列算法由于其计算效率高、存储开销小等优点，近年来被广泛应用于信息检索领域。然而，现有的散列算法由于散列优化过于宽松和缺乏有监督的语义标签，在大规模社会图像检索中表现不佳。在本文中，我们提出了一个有效的弱监督离散散列（WDH）来解决的局限性。我们制定了一个统一的弱监督哈希学习框架。该算法利用用户提供的社会标签有效地丰富了图像哈希码的语义，同时去除了其中的有害噪声。此外，代替放松散列优化，我们提出了一种有效的离散散列优化方法基于增广拉格朗日乘子（ALM）直接求解散列码没有量化信息的损失。在两个标准的社会图像数据集上的实验表明，该方法的性能优于几个国家的最先进的哈希技术相比，上级。
Hashing has been widely exploited for information retrieval recently, because of its high computation efficiency and low storage cost. However, many existing hashing methods cannot perform well on large-scale social image retrieval, due to the relaxed hash optimization and the lack of supervised semantic labels. In this paper, we propose an efficientWeakly-supervised Discrete Hashing(WDH) to solve the limitations. We formulate a unified weakly-supervised hash learning framework. It could effectively enrich the semantics of image hash codes with the freely obtained user-provided social tags and simultaneously remove their involved adverse noises. Furthermore, instead of relaxed hash optimization, we propose an efficient discrete hash optimization method based on Augmented Lagrangian Multiplier (ALM) to directly solve the hash codes without quantization information loss. Experiments on two standard social image datasets demonstrate the superior performance of the proposed method compared with several state-of-the-art hashing techniques.