Social-media Photo Appeal (SoPhoAppeal)

“社交媒体照片诉求”（SoPhoAppeal）项目旨在解决社交媒体网络背景下摄影图像的审美诉求。今天，数码相机可以在各种设备上使用，例如智能手机或各种类型和等级的专用相机。因此，每天有数以亿计的用户生成的图片通过不同的社交媒体平台发布。除了文字信息，图像已经成为数字社会交流的重要手段之一。同时，社交媒体中的图像为用户驱动和数据驱动相结合的图像审美诉求研究提供了一种新颖的系统形式。在SoPhoAppeal中，最终目标是提取图片内在美学品质对其喜爱和欣赏的贡献。它将研究审美吸引力如何与图片属性、图像语义、社交网络中的呈现以及社交媒体照片分享平台中的喜欢和观看行为相关。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。这类网站包括，例如，500px (https://500px.com), Flickr （https://www.flickr.com)，或1x (https://1x.com).Such）。社交媒体网站提供了大量的直接和间接的信息，如用户的摄影技能，人们喜欢什么，喜欢或拍摄的照片类型的偏好背景知识，一个带有语义信息的注释图像的大型数据库。并进一步匿名化社交媒体相关数据，包括个人摄影师的受欢迎程度。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信息，以及如何消除图像的受欢迎程度是来自社交媒体方面（相互关系，用户受欢迎程度等）还是来自图像的内在属性。在这里，数据匿名化方面也需要解决。此外，我们将在受控环境中进行测试，以在实验室观看环境中收集一组锚定图像的美学吸引力的真实数据。此外，我们将进行众包测试，从更接近实际使用环境的用户那里收集更多与审美吸引力相关的评分。结果将根据他们与社交媒体网络上观察到的喜欢的关系进行分析。使用不同的数据，将研究技术方面，图片属性，用户背景知识，社交网络属性，审美吸引力评级和社交媒体喜欢之间的关系。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欢预测的不同模型（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).