基于形状的措施改善了场景分类
IEEE transactions on pattern analysis and machine intelligence
|December 1, 2023
概括
深度神经网络经常忽略对象轮,与人类不同. 这项研究引入了检测轮线索的算法,通过突出这些未充分利用的视觉特征,显著改善了人类和人工智能模型的场景分类.
科学领域:
- 计算机视觉 计算机视觉
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 深度神经网络 (DNN) 显示对颜色和纹理的偏见,在图像分析中忽视了轮信息.
- 人类擅长使用轮来识别物体和场景,并遵循格斯塔尔特分组原则.
- 计算模型缺乏用于中级视觉的感知分组规则的实现.
研究的目的:
- 开发新的算法来检测复杂场景中的基于轮的线索.
- 为了计算实现Gestalt分组规则中等水平的视力.
- 评估轮线索对场景分类准确性的影响.
主要方法:
- 开发了用于检测复杂场景中的基于轮线索的算法.
- 使用中轴转换 (MAT) 基于分组规则来得分轮.
- 评估场景分类,并没有强调感知分组信息.
主要成果:
- 人类观察者和卷积神经网络 (CNN) 模型都在强调感知分组信息时获得了更高的准确性.
- 与新型措施加权轮相比,与未加权轮相比,加权轮显著提高了CNN模型的性能.
- 目前的CNN模型似乎没有提取或利用这些基于轮的分组线索,尽管它们很重要.
结论:
- 基于轮的感知分组线索对于准确的场景分类至关重要.
- 格萨特分组规则的计算实现提高了人工智能模型的性能.
- DNN可能需要修改架构或培训,以更好地利用轮信息.
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