通过基于标签的内容描述器进行透明的深度图像美学评估
概括
本研究介绍了基于标签的内容描述器 (TCD),用于使用可解释的语义特征进行透明的图像美学评估 (IAA). 新方法通过明确和隐含地描述图像内容,显著提高了审美预测的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 图像美学评估 (IAA) 的深度学习模型缺乏透明度,内部机制和学习的语义特征仍然不清楚.
- 现有的方法通常依赖于隐式学习的语义特征,阻碍了对有助于美学预测的全部理解.
研究的目的:
- 通过引入可解释的语义特征,为IAA开发一个更透明的深度学习框架.
- 通过使用人类可读的内容描述来提高IAA模型的可解释性.
主要方法:
- 建议基于标签的内容描述器 (TCD) 来量化图像与人类可读标签的相关性.
- 引入了使用预定义标签用于TCD生成的明确匹配过程.
- 开发了一个隐式匹配过程,以捕捉未被预定义标签覆盖的图像内容,集成到TCD中.
- 使用TCD生成器实现显式和隐式匹配过程.
主要成果:
- 一个使用基于预定义标签的TCD的IAA模型实现了0.767的斯皮尔曼等级相关系数 (SRCC),与最先进的方法可比.
- 将隐式匹配组件集成到TCD中,提高了IAA模型的性能,达到SRCC为0.817,超过了现有的IAA方法.
- TCD生成器证明了在定制验证集上的图像有效地分配了与摄影相关的标签.
结论:
- 基于标签的内容描述器为图像美学评估提供了一种透明和有效的方法.
- 在TCD中提出的明确和隐含匹配过程显著提高了IAA模型的准确性和可解释性.
- 该TCD生成器提供了一个强大的机制,用于描述图像内容语义,推进可解释AI在图像分析领域的领域.
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