多尺度结构复杂性作为视觉复杂性的定量度量.
Anna Kravchenko1, Andrey A Bagrov1, Mikhail I Katsnelson1
1Radboud University, Nijmegen, the Netherlands.
Perception
|November 20, 2025
概括
我们介绍了多尺度结构复杂性 (MSSC),这是视觉复杂性的新计算措施. MSSC有效量化图像复杂性,与人类感知相关,并提供一种直观,高效的方法.
科学领域:
- 计算机视觉 计算机视觉
- 认知科学 认知科学
- 图像分析 图像分析
背景情况:
- 定义和量化视觉复杂性是一个挑战.
- 人类对复杂性的感知是主观的,很难通过计算来建模.
研究的目的:
- 引入和验证用于量化视觉复杂性的多尺度结构复杂性 (MSSC) 测量.
- 评估MSSC和人类主观复杂度等级之间的相关性.
主要方法:
- 将MSSC测量方法应用于具有人类复杂度评分的图像开放数据集 (SAVOIAS).
- 将MSSC性能与其他计算复杂度指标进行比较.
主要成果:
- MSSC显示了与主观复杂性评级的强烈相关性.
- MSSC比现有措施更直观,在图像类别中更一致,并且在计算上更有效.
- 由于MSSC的多尺度性质,可以更深入地分析感知到的复杂性.
结论:
- MSSC提供了一个强大的和直观的方法来量化视觉复杂性.
- 这种测量与人类的感知很好地一致,在计算和一致性方面提供了优势.
- MSSC促进了对人类视觉复杂感知细微差别的进一步研究.
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