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相关概念视频

Aggregates Classification01:29

Aggregates Classification

317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317

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多重里曼核对大规模图像集分类和检索进行哈希.

Xiaobo Shen, Wei Wu, Xiaxin Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 2, 2024
    PubMed
    概括

    本研究引入了多重里曼核哈希 (MRKH) 用于大规模图像集分析. MRKH高效地模拟复杂的图像集,提高分类和检索准确度.

    科学领域:

    • 计算机科学 计算机科学
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 传统的图像集方法与大型数据集作斗争,在建模复杂性和效率方面面临挑战.
    • 现有的技术往往仅限于小型或中型图像集应用.

    研究的目的:

    • 开发一种用于有效和高效的图像集表示的新方法,特别是用于大规模应用.
    • 解决处理复杂图像集和计算需求的传统方法的局限性.

    主要方法:

    • 提出了多重里曼核哈希 (MRKH) 的建议,利用里曼的多元体和哈希用于图像集表示.
    • 采用多个内核学习框架来结合来自异质里曼的多元组的统计数据.
    • 为高效的计算和存储生成哈希代码,而不是连续的功能,通过代算法确保融合.

    主要成果:

    • 与最先进的方法相比,MRKH在5个基准数据集 (包括3个大规模数据集) 上表现优越.
    • 该方法在大规模的图像集分类和检索任务中实现了高精度.
    • 实验结果证实了MRKH的效率和可扩展性,具有线性计算复杂性.

    结论:

    • MRKH 为大规模图像集表示,分类和检索提供了有效和高效的解决方案.

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  • 里曼的多元体和哈希的集成为复杂的数据分析提供了一个强大的框架.
  • 拟议的方法显著提高了在现实世界的大规模场景中图像集分析的能力.