Will People Like Your Image? Learning the Aesthetic Space
Will People Like Your Image? Learning the Aesthetic Space
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DOI：
10.1109/wacv.2018.00226
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Katharina Schwarz;P. Wieschollek;H. Lensch
中科院分区：
文献类型：
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作者：
Katharina Schwarz;P. Wieschollek;H. Lensch
评价一幅图像看起来有多美观是一件非常复杂的事情，取决于大量不同的视觉因素。先前的工作已经通过在一维评级量表上排名来解决美学评级问题，例如，结合手工制作的属性。在本文中，我们提出了一个相当普遍的方法来映射审美愉悦与其所有的复杂性到一个自动的“审美空间”，以允许一个高度细粒度的决议。详细地说，利用深度学习，我们的方法直接将给定图像的编码学习到这个类似视觉美学的高维特征空间中。除了上述的视觉因素，个人判断的差异对照片的可爱性有很大的影响。如今，在线平台允许用户通过单击来“喜欢”或偏好特定内容。为了整合各种各样的人，我们利用这种多用户协议，并收集了一个包含380K图像（AROD）的广泛数据集，并获得了一个分数来评价给定照片的视觉愉悦程度。我们在用户研究中验证了我们的美学衍生模型。此外，没有任何额外的数据标签或手工制作的功能，我们在AVA基准数据集上实现了最先进的准确性。最后，由于我们的方法能够预测任何任意图像或视频的美学质量，我们展示了我们的结果，应用程序重新排序的照片集，捕捉最好的拍摄移动的设备和美学关键帧提取视频。
Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors. Previous work has tackled the aesthetic rating problem by ranking on a 1-dimensional rating scale, e.g., incorporating handcrafted attributes. In this paper, we propose a rather general approach to map aesthetic pleasingness with all its complexity into an automatically "aesthetic space" to allow for a highly fine-grained resolution. In detail, making use of deep learning, our method directly learns an encoding of a given image into this highdimensional feature space resembling visual aesthetics. In addition to the mentioned visual factors, differences in personal judgments have a substantial impact on the likeableness of a photograph. Nowadays, online platforms allow users to "like" or favor particular content with a single click. To incorporate a vast diversity of people, we make use of such multi-user agreements and assemble an extensive data set of 380K images (AROD) with associated meta information and derive a score to rate how visually pleasing a given photo is. We validate our derived model of aesthetics in a user study.Further, without any extra data labeling or handcrafted features, we achieve state-of-the-art accuracy on the AVA benchmark data set. Finally, as our approach is able to predict the aesthetic quality of any arbitrary image or video, we demonstrate our results on applications for resorting photo collections, capturing the best shot on mobile devices and aesthetic key-frame extraction from videos.