Fast Supervised Discrete Hashing and its Analysis
Fast Supervised Discrete Hashing and its Analysis
复制标题
DOI：
--
复制
发表时间：
2016-11
期刊：
影响因子：
--
通讯作者：
G. Koutaki;K. Shirai;Mitsuru Ambai
中科院分区：
文献类型：
--
作者：
G. Koutaki;K. Shirai;Mitsuru Ambai
在本文中，我们提出了一种基于学习的监督离散散列方法。二进制哈希广泛用于大规模图像检索以及视频和文档搜索，因为二进制代码的紧凑表示对于数据存储是必不可少的，并且对于使用位操作的查询搜索是合理的。最近提出的监督离散散列（SDH）有效地解决了交替优化和离散循环坐标下降（DCC）方法的混合整数规划问题。我们表明，SDH模型可以简化而不降低性能的基础上，一些初步的实验，我们称之为近似模型的“快速SDH”（FSDH）模型。我们分析了FSDH模型，并给出了它的数学精确解，与SDH相比，我们的模型不需要交替优化算法，也不依赖于初始值。FSDH也比迭代量化（ITQ）更容易实现。在大规模数据库上的实验结果表明，FSDH在查准率、查全率和计算时间方面优于传统SDH。
In this paper, we propose a learning-based supervised discrete hashing method. Binary hashing is widely used for large-scale image retrieval as well as video and document searches because the compact representation of binary code is essential for data storage and reasonable for query searches using bit-operations. The recently proposed Supervised Discrete Hashing (SDH) efficiently solves mixed-integer programming problems by alternating optimization and the Discrete Cyclic Coordinate descent (DCC) method. We show that the SDH model can be simplified without performance degradation based on some preliminary experiments; we call the approximate model for this the "Fast SDH" (FSDH) model. We analyze the FSDH model and provide a mathematically exact solution for it. In contrast to SDH, our model does not require an alternating optimization algorithm and does not depend on initial values. FSDH is also easier to implement than Iterative Quantization (ITQ). Experimental results involving a large-scale database showed that FSDH outperforms conventional SDH in terms of precision, recall, and computation time.