探索MPSO-两个阶段分类优化模型,用于低质量和复杂语义的场景图像
Kexin Liu1, Rong Wang1, Xiaoou Song1
1Department of Information Engineering, Engineering University of PAP, Xi'an 710086, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究介绍了一种基于MPSO的两阶段模型,用于高精度的低质量场景图像分类,其性能优于现有方法. 该模型在实际的户外环境和受天气影响的环境中表现出有效性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 当前复杂的场景分类方法主要集中在高清图像上,忽视了低质量和低分辨率的数据集.
- 现有的对噪音图像或特定集的研究不涉及实际的低分辨率场景图像,限制了实际应用.
研究的目的:
- 为低质量的场景图像提出一个实用,高精度的分类模型.
- 开发一个基于MPSO (多粒子群优化) 的两阶段分类优化算法.
主要方法:
- 提出了一个基于MPSO的两阶段分类优化算法模型.
- 使用国际公认的场景数据集和自建的低质量的新闻场景框架进行了比较实验.
- 在受照明和天气条件影响的场景设置上进行了适应性测试.
主要成果:
- 拟议的MPSO模型实现了比21种现有方法更高的准确性,特别是与ResNet-ELM相比,在15场景数据集上的性能提高了1.54%.
- 模型的预重建阶段显示出明显的改进率,特别是户外场景,与现有的六种预处理方法相比.
- 该模型证明适用于受天气影响的场景分类,在适应性测试中平均精度提高了1.42%.
结论:
- 拟议的两阶段MPSO模型为低质量场景图像的高精度分类提供了实用和有效的解决方案.
- 模型的重建前阶段对于提高性能至关重要,特别是在具有挑战性的户外和可变天气条件下.
- 这项研究通过解决低质量的场景图像分类被忽视的领域来推进该领域.
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