关系一致性诱导自主监督的哈希用于图像检索.
IEEE transactions on neural networks and learning systems
|November 23, 2023
概括
这项研究引入了关系一致性诱导的自我监督哈希 (RCSH) 以实现高效的大规模图像检索. RCSH学习数据关系以生成紧的哈希代码,显著优于现有方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 大规模的图像检索需要有效的方法来搜索庞大的数据集.
- 现有的哈希技术往往难以在图像数据中捕捉复杂的语义关系.
- 自主监督学习为学习没有手动注释的表示提供了一个有希望的途径.
研究的目的:
- 提出一种新的散列框架,关系一致性诱导的自我监督散列 (RCSH),用于大规模的图像检索.
- 为了有效地捕获和保存隐藏和哈明空间中的语义数据关系.
- 提高图像检索系统的准确性和概括能力.
主要方法:
- RCSH探索了不同特征空间的数据样本之间的关系一致性.
- 它通过识别原型来学习数据关系,这些原型在潜在空间中组合了类似的图像.
- 引入了双原型对比损失,以对准潜伏空间和哈明空间之间的原型分配,确保一致的数据与原型和数据与数据之间的关系.
主要成果:
- 拟议的RCSH方法在四个基准图像检索数据集上显著优于最先进的方法.
- 在域外检索任务中,RCSH表现出强的性能,这表明它具有出色的概括能力.
- 该框架成功地学习可靠的数据关系,并将其保存为紧的哈希代码.
结论:
- RCSH提供了一种有效的自我监督的哈希方法,用于大规模的图像检索.
- 该方法捕获语义结构的能力提高了检索准确性和稳定性.
- 该框架的通用化能力使其适用于各种现实世界的应用.
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