2022年 02期

摘要(Abstract)：

针对图像检索任务中部分监督学习部署困难，以及一般无监督学习没有利用监督信息导致检索性能劣化的问题，提出一种基于正态分布的距离保持哈希的无监督框架，使生成的哈希码保持图像的原始距离关系，在检索结果中尽可能保留相似的图像；距离保持哈希使用正态分布框架约束生成的连续码保持原始特征的距离关系，将图像的语义信息尽可能保留到哈希码中，并使用标准化欧氏距离进行特征向量的相似度计算，解决直接使用传统欧氏距离作为损失时神经网络梯度下降产生的网络波动问题。实验结果表明，与其他模型相比，距离保持哈希在大规模图像检索中检索性能更好。

关键词(KeyWords)： 图像检索;距离保持哈希;正态分布;语义信息;标准化欧氏距离

基金项目(Foundation):国家自然科学基金项目(61841602);; 山东省自然科学基金项目(ZR2018PF005)

作者(Author): 闫冰琦,田爱奎,吴楠楠,孙妍,王振

DOI: 10.13349/j.cnki.jdxbn.20211025.001

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