语义和相关性解图形卷积用于多标签图像识别.
IEEE transactions on neural networks and learning systems
|November 30, 2023
概括
本研究引入了一种用于多标签图像识别 (MLR) 的新方法,该方法有效地使用图像特定标签相关性. 拟议的方法通过专注于每个图像内的独特关系来提高准确性,优于对基准数据集的现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 多标签图像识别 (MLR) 面临着对象封闭和小物体大小的挑战.
- 现有的MLR方法通常依赖于全球标签相关性,忽视图像特定关系.
- 这限制了准确预测复杂场景标签的能力.
研究的目的:
- 为MLR提出一种新的方法,利用图像特定标签相关性.
- 解决MLR中基于全球相关性的方法的局限性.
- 为了提高MLR的准确性,特别是对于具有封闭或小物体的具有挑战性的情况.
主要方法:
- 介绍了语义和相关性解图形卷积 (SCD-GC) 方法.
- 开发了一个语义解模块,以提取类别性语义特征作为图的节点.
- 创建了一个关联解模块,以提取特定图像的标签相关性作为图形边缘,形成图形卷积的特定图像图形.
主要成果:
- 该SCD-GC方法有效地解开图像中的主导标签相关性.
- 图形特定图形上的图形卷曲增强了具有弱视觉表现的标签的挖掘.
- 在包括MS-COCO,PASCAL-VOC,NUS-WIDE和VG-500在内的基准数据集上取得了优异的结果.
结论:
- 拟议的SCD-GC方法通过利用图像特定的标签相关性,显著提高了多标签图像识别.
- 这种方法提供了一种更有效的方式来处理复杂的场景和具有挑战性的物体特征在MLR.
- 该方法表现出强大的性能和现实世界应用的潜力.
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