Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Aggregates Classification01:29

Aggregates Classification

1.0K
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...
1.0K
Methods of Classification and Identification01:28

Methods of Classification and Identification

2.3K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
2.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Predictive Value of the Waist-to-Height Ratio and HbA1c Product for Incident Stroke: A Stratified Analysis by Blood Pressure Status.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association·2026
Same author

PmD479 is an Unutilized Gene for Powdery Mildew Resistance in Common Wheat.

Plant biotechnology journal·2026
Same author

A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.

Scientific reports·2026
Same author

Lateral humeral condyle combined with ulnar olecranon fractures in pediatric patients: A retrospective study of treatment and outcomes.

Medicine·2026
Same author

Energy-preserving shifted bipartite graph learning for unpaired large-scale multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: May 4, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

为图像集分类提供一致和特定的哈希.

Xingfeng Li1, Yuan Sun2, Xuedong Li3

  • 1School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China; Department of Computer Science, Nanjing University of Science and Technology Nanjing, Nanjing, 210094, China.

Neural networks : the official journal of the International Neural Network Society
|May 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于图像集分类 (ISC) 的一致和特定哈希 (CSH). 通过学习紧的哈希代码,CSH提高了效率和准确性,在分类和速度方面超过了现有的方法.

关键词:
散列聚合策略的散列聚合策略.图像集的分类图像集的分类样本特定的哈希代码是特定的哈希代码.集一致的哈希代码 集一致的哈希代码

更多相关视频

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE
13:28

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

Published on: May 16, 2017

50.2K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

930

相关实验视频

Last Updated: May 4, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE
13:28

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

Published on: May 16, 2017

50.2K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

930

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:

  • 图像集分类 (ISC) 利用多个图像来提高分类准确度,而不是单个图像方法.
  • 现有的ISC方法通常面临着大数据集的计算挑战.
  • 基于哈希的方法提供较低的计算成本,但可能会忽视特定集合的属性.

研究的目的:

  • 为图像集分类开发一种高效和有效的基于散列的方法.
  • 解决ISC现有哈希方法的局限性,特别是关于一致性和特异性的问题.
  • 改进图像集内部和图像集之间的语义信息的挖掘.

主要方法:

  • 建议为ISC提供一致和特定的哈希 (CSH).
  • 利用哈达马德矩阵进行预先计算的集合一致哈希代码,以最大限度地实现跨集合的哈明距离.
  • 学习了样本特定的哈希代码,并采用了哈希聚合策略来实现集内部紧性和集内部可分离性.

主要成果:

  • 与现有的图像集分类方法相比,CSH表现出优越的性能.
  • 拟议的方法在分类准确度方面取得了显著的改进.
  • CSH还显示了运行时间的减少,这表明了计算效率.

结论:

  • CSH有效地解决了以往基于散列的ISC方法的局限性.
  • 该方法通过保持集内部的紧性和集间的分离性,成功地挖掘了语义信息.
  • CSH为大规模图像集分类提供了一个有希望的,计算效率高的解决方案.