用知识对图像进行分类的数据集
Franck Anaël Mbiaya1,2, Christel Vrain1, Frédéric Ros2
1University Orleans, INSA Centre Val de Loire, LIFO, EA 4022, France.
Data in brief
|September 27, 2024
概括
这项研究引入了新的图像分类数据集,结合了先前的知识,提高了有限数据的性能. 常见项目集采矿从属性中提取规则,用于增强的深度学习模型.
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:
- 深度学习在大数据集的图像分类方面表现出色.
- 在有限的数据下,性能显著下降.
- 对深层架构而言,细粒度的分类是具有挑战性的.
研究的目的:
- 解决深度学习在低数据和细粒度图像分类场景中的局限性.
- 引入结合先验知识的新型数据集.
- 促进对利用图像分类事先知识的研究.
主要方法:
- 数据集是从现有的多标签,多类分类或物体检测数据中构建的.
- 频繁的封闭项目的采矿被用来生成类和属性.
- 以这些属性为基础的规则形式提取先验知识.
主要成果:
- 开发的数据集集整合了先验知识,增强了图像分类能力.
- 该方法允许从原始数据创建结构化知识.
- 规则生成算法详细说明了实际应用.
结论:
- 将先验知识集成到数据集中对于改善数据稀缺和复杂分类任务中的深度学习性能至关重要.
- 拟议的方法为生成此类数据集提供了一种可行的方法.
- 这项工作扩大了对知识增强图像分类研究的可用资源.
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