Social-media Photo Appeal (SoPhoAppeal)

“社交媒体照片吸引力”项目（SoPhoAppeal）解决了社交媒体网络背景下摄影图像的美学吸引力。如今，数码相机可用于多种设备，例如智能手机或所有类型和等级的专用相机。因此，每天有数亿张用户生成的图片通过不同的社交媒体平台发布。除了文本信息之外，图像已经成为数字社交通信的关键手段之一。与此同时，社交媒体中的图像也为用户和数据驱动的图像美学研究提供了一种新的系统形式。SoPhoAppeal的最终目标是提取图片内在美学质量对喜爱和欣赏的贡献。它将研究审美情趣如何与图片属性，图像语义，在社交网络中的演示和社交媒体照片共享平台中的喜好和观看行为。为了进行这种以美学为导向的分析，将主要研究专门用于摄影的社交媒体平台，其中照片被视为一种艺术，而不是分享摄影师生活事件的方式。这种类型的网站包括例如500px（500px.com）、Flickr（1x.com）或1x（https：//www.example.com）。这样的社交媒体网站提供关于诸如用户的摄影技能、关于人们喜欢什么的信息、关于喜欢或拍摄的照片的类型的偏好的背景知识、具有语义信息的注释图像的大型数据库以及包括个人摄影师的流行度的进一步匿名化的社交媒体相关数据等方面的大量直接和间接信息。www.flickr.com该项目的目标之一是研究如何从大量的元数据中提取有关图像吸引力的有价值的信息，以及如何消除图像受欢迎程度是否来自社交媒体方面（相互关系，用户受欢迎程度等）的歧义，或者从图像的内在属性。在这里，数据匿名化的各个方面也需要得到解决。此外，我们将在受控环境中进行测试，以收集实验室观看背景下一组锚图像的美学吸引力的真实数据。此外，我们将进行众包测试，以收集更接近用户实际使用环境的用户对美学吸引力的进一步评级。结果将根据其与社交媒体网络中观察到的喜好的关系进行分析。使用不同的数据，将研究技术方面，图片属性，用户的背景知识，社交网络属性，审美情趣评级和社交媒体喜好之间的关系。该分析的结果将用于研究自动的、基于机器学习的吸引力和喜好预测的不同模型（统计的、基于深度学习的和混合的）。

The project "Social-media Photo Appeal" (SoPhoAppeal) addresses the aesthetic appeal of photographic images in the context of social media networks. Today, digital cameras are available in a multitude of devices such as smartphones or dedicated cameras of all types and grades. As a consequence, hundreds of millions of user-generated pictures are published via different social media platforms every day. Beyond textual information, images have become one of the key means for digital social communication. At the same time, images in social media enable a novel systematic form of combined user- and data-driven image aesthetic appeal research.In SoPhoAppeal, the ultimate goal is to extract the contribution of the intrinsic aesthetic quality of a picture to its liking and appreciation. It will be studied how aesthetic appeal relates to the picture properties, the image semantics, the presentation in the social network and the liking and viewing behaviour in the social media photo-sharing platform. To perform such aesthetics-oriented analysis, social media platforms dedicated to photography will primarily be studied, where photos are considered as an art rather than a way to share events of the photographer's life. This type of websites include, for example, 500px (https://500px.com), Flickr (https://www.flickr.com), or 1x (https://1x.com).Such social media sites provide a large amount of direct and indirect information on aspects such as the users' skills in photography, information about what people like, background knowledge on preferences in terms of types of photos liked or taken, a large database of annotated images with semantic information, and further anonymized social-media-related data including the popularity of individual photographers. One of the goals of the project is to study how valuable information about image appeal can be extracted from the large amount of meta-data, and how it can be disambiguated whether the popularity of an image comes from social-media aspects (interrelations, popularity of user, etc.), or from the intrinsic properties of the images. Here, aspects of data anonymization will need to be addressed, too. In addition, we will conduct tests in a controlled environment to collect ground-truth data on aesthetic appeal for a set of anchor images in a lab-viewing context.Moreover, we will conduct crowd-sourcing tests to collect further aesthetic-appeal-related ratings from users closer to their real-life usage contexts. The results will be analyzed in terms of their relation to the liking observed in the social media networks. Using the different data, the relationship between technical aspects, picture properties, background knowledge of the user, social network properties, aesthetic appeal ratings and social media liking will be studied. The results of this analysis will be used to study different models for automatic, machine-learning-based appeal and liking prediction (statistical, deep-learning-based and hybrid).