In spite of the recent advancements of deep learning based techniques, automatic photo aesthetic assessment still remains a challenging computer vision task. Existing approaches used to focus on providing a single aesthetic score or category (“good” or “bad”) of photograph, rather than quantifying “goodness” or “badness”. The existing algorithms often ignore the importance of different attributes contributing to the artistic quality of the photograph. To obtain the human-interpretability of aesthetic score of photo, we advocate learning the aesthetic attributes alongwith the prediction of the general aesthetic score. We propose a multi-task deep CNN, that collectively learns aesthetic attributes alongwith a general aesthetic score for the photograph. To understand the mathematical representation of the attributes in the proposed model, a visualization technique is proposed using back propagation of gradients. These visualization of attributes correspond to the location of objects in the images in order to find out which part of an image “triggers” the classification outcome, thus providing the insights about the model's understanding of these attributes. This paper proposes an aesthetic feature vector based on the relative foreground position of the object in the image. The proposed aesthetic features outperform the state-of-art methods especially for Rule of Thirds attribute.

中文翻译：

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使用深层CNN衡量摄影美学

尽管基于深度学习的技术最近有所发展，但是自动照片美学评估仍然是一项具有挑战性的计算机视觉任务。现有方法集中于提供照片的单个美学评分或类别（“好”或“坏”），而不是量化“好”或“坏”。现有的算法经常忽略了有助于照片艺术品质的不同属性的重要性。为了获得照片的审美得分的人类可解释性，我们主张学习审美属性以及对一般审美得分的预测。我们提出了一种多任务深度CNN，该方法可以共同学习美学属性以及照片的总体美学得分。要了解所建议模型中属性的数学表示形式，提出了一种使用梯度反向传播的可视化技术。这些属性的可视化对应于图像中对象的位置，以便找出图像的哪个部分“触发”了分类结果，从而提供了有关模型对这些属性的理解的见解。本文提出了一种基于图像中物体相对前景位置的美学特征向量。所提出的美学特征优于最新方法，特别是对于“三分法则”属性。对这些属性的理解。本文提出了一种基于图像中物体相对前景位置的美学特征向量。所提出的美学特征优于最新方法，特别是对于“三分法则”属性。对这些属性的理解。本文提出了一种基于图像中物体相对前景位置的美学特征向量。所提出的美学特征优于最新方法，特别是对于“三分法则”属性。