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Least square regularized spectral hashing for similarity search

机译：最小二乘正则化频谱哈希用于相似度搜索

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摘要

Among the existing hashing methods, spectral hashing (SpH) and self-taught hashing (STH) are considered as the state-of-the-art works. However, two such methods still have some drawbacks. For example, when generating the extension of out-of-sample, SpH makes assumption that data follows uniform distribution but it is impractical. As to STH, its hash functions are obtained by training SVM classifier bit-by-bit, which will lead to ten-fold increase in training time. Moreover, they both suffer overfitting issue. To conquer those drawbacks, we propose a new hashing method, also called LS_SPH, which adopts a unified objective function to obtain the binary embeddings of training objects and hash functions for predicting hash code of test object. Integrating two such processes together will bring in two advantages: (1) It can highly decrease the time complexity of offline stage for training hash codes and hash function due to not requiring extra time for learning hash function. (2) The overfitting issue can be successfully avoided because the empirical loss function associated with hash function is served as the regularization item in objective function in this method. The extensive experiments show that the LS_SPH is superior to the state-of-the-art hashing methods such as SpH and STH on the whole.
机译：在现有的哈希方法中，频谱哈希（SpH）和自学哈希（STH）被认为是最新技术。但是，两种这样的方法仍然有一些缺点。例如，在生成样本外扩展时，SpH假设数据遵循均匀分布，但不切实际。对于STH，其散列函数是通过逐位训练SVM分类器获得的，这将导致训练时间增加十倍。而且，它们都遭受过拟合的问题。为了克服这些缺点，我们提出了一种新的哈希方法，也称为LS_SPH，该方法采用统一的目标函数来获取训练对象的二进制嵌入，并使用哈希函数来预测测试对象的哈希码。将两个这样的过程集成在一起将带来两个好处：（1）由于不需要额外的时间来学习哈希函数，因此可以大大降低用于训练哈希码和哈希函数的离线阶段的时间复杂度。 （2）由于与哈希函数关联的经验损失函数被用作目标函数中的正则项，因此可以成功避免过拟合问题。广泛的实验表明，LS_SPH总体上优于诸如SpH和STH之类的最新哈希方法。

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  • 来源
    《Signal processing》 |2013年第8期|2265-2273|共9页
  • 作者

    Fuhao Zou; Cong Liu; Hefei Ling; Hui Feng; Lingyu Yan; Dan Li;

  • 作者单位

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Similarity search; Spectral hashing; Least square; Trace optimization;

    机译：相似度搜索;频谱哈希最小二乘法;迹线优化;

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