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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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参数自适应对比哈希用于多媒体检索.

Yunfei Chen1, Yitian Long2, Zhan Yang1

  • 1Big Data Institute, School of Computer Science and Engineering, Central South University, ChangSha, Hunan, 410000, China.

Neural networks : the official journal of the International Neural Network Society
|November 24, 2024
PubMed
概括

这项研究引入了参数自适应对比哈希 (PACH) 以实现高效的多媒体检索. PACH克服了无监督哈希学习中的超参数调整挑战,提高了语义相似性和检索准确性.

关键词:
快速的参数适应性适应性多源异质的多源异质的多媒体对比的哈希.多媒体检索检索

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科学领域:

  • 计算机科学 计算机科学
  • 信息检索 信息检索
  • 机器学习 机器学习

背景情况:

  • 互联网数据的扩散需要有效的信息检索方法.
  • 哈希学习是多媒体检索的主流技术,因为它的速度和效率.
  • 无监督的多媒体哈希学习方法与超参数调整和语义相似性指导扎.

研究的目的:

  • 为多媒体检索提出一种新的参数自适应对比哈希 (PACH) 方法.
  • 解决现有的无监督哈希学习技术的局限性.
  • 提高多媒体信息检索的准确性和效率.

主要方法:

  • 开发了一个快速参数适应 (FPA) 模块,使用强化学习进行自动超参数优化.
  • 将FPA模块与多媒体对比哈希 (MCH) 模块集成,以探索跨模式的语义一致性.
  • 设计了全面的实验,以评估与最先进的技术对比拟的PACH方法.

主要成果:

  • 在多媒体检索任务中,PACH方法表现出卓越的性能.
  • FPA模块有效地识别了最佳参数组合,简化了调整过程.
  • 该MCH模块成功地将哈希代码与跨模态语义信息进行了丰富.

结论:

  • 拟议的PACH方法为多媒体检索挑战提供了有效的解决方案.
  • 通过解决超参数调整和语义相似性问题,PACH提高了检索准确性.
  • 该方法为无监督的多媒体哈希学习提供了强大而高效的方法.