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Nonlinear Sparse Hashing

机译：非线性稀疏散列

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摘要

To facilitate fast similarity search, this paper proposes to encode the nonlinear similarity and image structure as compact binary codes. Rather than adopting single matrix as projection in the literature, we employ a nonlinear transformation in the form of multilayer neural network to generate binary codes to capture the local structure between data samples. Specifically, we train the network such that the quantization loss is minimized and the variance over all bits is maximized. In addition, we capture the salient structure of image samples at the abstract level with sparsity constraint and inherit the generalization power to unseen samples. Furthermore, we incorporate the supervisory label information into the learning procedure to take advantage of the manual label. To obtain the desired binary codes and the parameterized nonlinear transformation, we optimize the formulated objective problem over each variable with an iterative alternating method. To validate the efficacy of the proposed hashing approach, we conduct experiments on three widely used datasets, namely CIFAR10, MNIST, and SUN397, by comparing with several recent proposed hashing methods.
机译：为了促进快速相似度搜索，本文提出将非线性相似度和图像结构编码为紧凑的二进制代码。我们没有采用文献中的单个矩阵作为投影，而是采用多层神经网络形式的非线性变换来生成二进制代码，以捕获数据样本之间的局部结构。具体来说，我们训练网络以使量化损失最小化，并且所有位的方差最大化。此外，我们在具有稀疏性约束的抽象级别上捕获图像样本的显着结构，并将泛化能力继承给看不见的样本。此外，我们将监督标签信息纳入学习过程，以利用手动标签的优势。为了获得所需的二进制代码和参数化的非线性变换，我们使用迭代交替方法对每个变量优化了制定的目标问题。为了验证所提出的散列方法的有效性，我们通过与几种最新提出的散列方法进行比较，对三个广泛使用的数据集（即CIFAR10，MNIST和SUN397）进行了实验。

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  • 来源
    《IEEE transactions on multimedia》 |2017年第9期|1996-2009|共14页
  • 作者

    Zhixiang Chen; Jiwen Lu; Jianjiang Feng; Jie Zhou;

  • 作者单位

    Department of Automation, with the State Key Laboratory of Intelligent Technologies and Systems, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology, Beijing, China;

    Department of Automation, with the State Key Laboratory of Intelligent Technologies and Systems, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology, Beijing, China;

    Department of Automation, with the State Key Laboratory of Intelligent Technologies and Systems, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology, Beijing, China;

    Department of Automation, with the State Key Laboratory of Intelligent Technologies and Systems, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology, Beijing, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Binary codes; Encoding; Training; Neural networks; Semantics; Quantization (signal); Sparse matrices;

    机译：二进制码编码训练神经网络语义量化（信号）稀疏矩阵;

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