Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels. To apply deep hashing on datasets without labels, we propose a deep self-taught hashing algorithm (DSTH), which generates a set of pseudo labels by analyzing the data itself, and then learns the hash functions for novel data using discriminative deep models. Furthermore, we generalize DSTH to support both supervised and unsupervised cases by adaptively incorporating label information. We use two different deep learning framework to train the hash functions to deal with out-of-sample problem and reduce the time complexity without loss of accuracy. We have conducted extensive experiments to investigate different settings of DSTH, and compared it with state-of-the-art counterparts in six publicly available datasets. The experimental results show that DSTH outperforms the others in all datasets.

中文翻译：

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用于图像检索的深度自学哈希


哈希算法因其紧凑的二进制代码和快速的距离计算而被广泛用于加速图像检索。与深度学习的结合通过学习准确的表示和复杂的哈希函数来提高哈希的性能。到目前为止，深度哈希最引人注目的成功主要涉及需要标签的判别模型。为了在没有标签的数据集上应用深度哈希，我们提出了一种深度自学哈希算法（DSTH），该算法通过分析数据本身生成一组伪标签，然后使用判别性深度模型学习新数据的哈希函数。此外，我们通过自适应地合并标签信息来推广 DSTH 以支持有监督和无监督的情况。我们使用两种不同的深度学习框架来训练哈希函数来处理样本外问题并在不损失准确性的情况下降低时间复杂度。我们进行了广泛的实验来研究 DSTH 的不同设置，并将其与六个公开数据集中的最先进的同行进行比较。实验结果表明 DSTH 在所有数据集中都优于其他模型。