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随机在线散列用于交叉模式检索
IEEE transactions on neural networks and learning systems
|December 4, 2023
概括
本研究介绍了随机在线散列 (ROH),一种新的监督跨模态散列方法. ROH增强了语义信息的保存和模式特定的功能嵌入,以实现高效的大规模多媒体数据库搜索.
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:
- 在大型多媒体数据库中进行高效搜索,监督的交叉模式哈希方法至关重要.
- 使用相似度矩阵或矩阵因子化的现有方法在可扩展性和保存模式特定信息方面存在局限性.
- 需要先进的方法,可以有效地整合语义相关性和模式特定特征.
研究的目的:
- 提出一种新的监督交叉模式哈希方法,即随机在线哈希 (ROH).
- 解决现有方法在保存语义信息和模式特定特征方面的局限性.
- 开发一种有效的方法来动态处理实时数据.
主要方法:
- 引入了线性桥梁策略,以简化对智的相似性因子化成线性优化.
- 使用过桥矩阵建立哈希代码和标签之间的双向线性关系.
- 提出了最大自身价值方向 (MED) 嵌入方法,以保存关键的模式特定信息,并采用了动态数据处理的在线结构.
主要成果:
- 桥梁矩阵有效地保留语义相似性,并减少类似标记样本之间的语义距离.
- 媒体嵌入方法成功地将关键信息嵌入到模式特定的哈希代码中.
- 对基准数据集的广泛实验表明,ROH的性能优于现有的最先进的交叉模式哈希方法.
结论:
- 通过改进语义信息的保存和模式特定的功能集成,ROH为监督的交叉模式哈希提供了有效的解决方案.
- 拟议的线性桥梁策略和MED嵌入方法提高了跨模式散列的性能和可扩展性.
- 在线结构使ROH适用于具有动态数据流的实时应用程序.
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