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Non-negative locality-constrained vocabulary tree for finger vein image retrieval

机译：用于手指静脉图像检索的非负局限性词汇树

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摘要

Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree model-based image retrieval approach because of its good scalability and high efficiency.However, there is a large accumulative quantization error in the vocabulary tree (VT)model that may degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performance and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performance than other state-of-the-art methods, while maintaining low time complexity.
机译：手指静脉图像检索是一种生物识别技术，最近引起了很多关注。它具有减少搜索空间的潜力，并且最近吸引了大量的研究工作。由于生物识别数据库中的大量图像以及缺乏有效的检索方案，这是一个具有挑战性的问题。由于具有良好的可扩展性和较高的效率，因此我们采用了基于分层词汇树模型的图像检索方法，但是词汇树（VT）模型中存在较大的累积量化误差，这可能会降低检索精度。为了解决此问题，我们通过引入非负局部性约束的约束：基于非负局部性约束的词汇树的图像检索模型，改进了VT模型中的矢量量化编码。所提出的方法可以有效地提高编码性能和局部特征的判别能力。在大型融合手指静脉数据库上进行的大量实验证明了我们编码方法的优越性。实验结果还表明，我们的检索策略在保持较低时间复杂度的同时，比其他最新方法具有更好的性能。

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  • 来源
    《Frontiers of computer science in China》 |2019年第2期|318-332|共15页
  • 作者

    Su Kun; Yang Gongping; Yang Lu; Su Peng; Yin Yilong;

  • 作者单位

    Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China|Shandong Univ Weihai, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China;

    Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China;

    Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Shandong, Peoples R China;

    Dali Univ, Sch Math, Dali 671000, Peoples R China;

    Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China|Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Shandong, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    non-negative locality-constrained vocabulary tree; finger vein image retrieval; large scale; inverted indexing;

    机译：非负局限性词汇树;手指静脉图像检索;大规模;反索引;

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