Fast Supervised Discrete Hashing
Fast Supervised Discrete Hashing
复制标题
快速监督离散散列
DOI：
10.1109/tpami.2017.2678475
复制
发表时间：
2018-02-01
影响因子：
23.6
通讯作者：
Tan, Tieniu
中科院分区：
文献类型：
--
作者：
Gui, Jie;Liu, Tongliang;Tan, Tieniu
基于学习的哈希算法是“热门话题”，因为它们可以大大增加现有方法的规模。本文在“监督离散散列”（SDH）的基础上，提出了一种新的基于学习的散列方法--“快速监督离散散列”（FSDH）。将训练样本（或哈希码）回归到相应的类标签广泛用于普通最小二乘回归。FSDH没有采用这种方法，而是使用一种非常简单但有效的训练样本的类标签回归到相应的哈希码来加速算法。据我们所知，这种策略以前没有用于哈希。传统的SDH将优化分解为三个子问题，其中最关键的子问题-二进制哈希码的离散优化-使用迭代离散循环坐标下降（DCC）来解决，这是耗时的。然而，FSDH具有封闭形式的解决方案，并且仅需要单个而不是迭代的哈希码求解步骤，这是高效的。此外，FSDH通常比SDH更快地求解最小二乘回归的投影矩阵，使得FSDH通常比SDH更快。例如，我们的结果表明，FSDH是大约12倍的速度比SDH的哈希位数为128的CIFAR-10数据库，和FSDH是大约151倍的速度比FastHash的哈希位数为64的MNIST数据库。实验结果表明，FSDH不仅速度快，而且性能优于其他比较方法。
Learning-based hashing algorithms are "hot topics" because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called "fast supervised discrete hashing" (FSDH) based on "supervised discrete hashing" (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.