论文标题
建模，量化和预测图像美学的主观性
Modeling, Quantifying, and Predicting Subjectivity of Image Aesthetics
论文作者
论文摘要
评估图像美学是一项具有挑战性的计算机视觉任务。原因之一是美学偏好是高度主观的，并且在某些图像中可能会有很大的不同。因此，正确对此\ textit {主观性}进行正确建模和量化很重要，但是解决此问题并没有太多努力。在本文中，我们提出了一个新型的统一概率框架，可以根据主观逻辑对主观美学偏好进行建模和量化。在此框架中，评级分配被建模为Beta分布，从中，绝对令人愉悦，绝对令人不快和不确定的概率可以得到。我们使用不确定的概率来定义主观性的直观指标。此外，我们提出了一种学习深度神经网络以预测图像美学的方法，该方法可有效地通过实验改善主观性预测的性能。我们还提出了一个应用程序方案，该方案对基于美学的图像建议有益。
Assessing image aesthetics is a challenging computer vision task. One reason is that aesthetic preference is highly subjective and may vary significantly among people for certain images. Thus, it is important to properly model and quantify such \textit{subjectivity}, but there has not been much effort to resolve this issue. In this paper, we propose a novel unified probabilistic framework that can model and quantify subjective aesthetic preference based on the subjective logic. In this framework, the rating distribution is modeled as a beta distribution, from which the probabilities of being definitely pleasing, being definitely unpleasing, and being uncertain can be obtained. We use the probability of being uncertain to define an intuitive metric of subjectivity. Furthermore, we present a method to learn deep neural networks for prediction of image aesthetics, which is shown to be effective in improving the performance of subjectivity prediction via experiments. We also present an application scenario where the framework is beneficial for aesthetics-based image recommendation.
