Department of Computing, Electronics and Mathematics,
Plymouth University, UK
In the last decade, creating and sharing videos online has become a mainstream movement and has led to some creators generating one personal video per day, also called daily vlogging. Although robust solutions exist to suggest photographs based on aesthetic criteria, the rising number of online videos created and watched means that such recommendation systems are required more than ever for videos. The main purpose of this paper is to transfer the skill of computational aesthetic classification of photographs to videos while developing new ways of investigating video creation. Using a dataset of photographs rated on aesthetic criteria by an internet community and recently developed feature extraction algorithms, the computational aesthetic classifier is capable of state-of-the-art photograph classification depending on aesthetic preferences learnt from people’s ratings. On a test set of YouTube videos, the same system then displays satisfying aesthetic classification results that consist of an attempt to match the provided human aesthetic quality ratings. Achieving a transfer of skill from photograph to video classification, the computational classifier is used to analyze the evolution of aesthetics in feature films; this highlighted the aesthetic classifier’s visual preferences and caused some interesting patterns to emerge that were related to filmmakers’ decisions. Aesthetic classification makes it possible to observe the evolution of aesthetics over the careers of daily content creators thanks to their abundant and regular online video content. It can aid the investigation into the impact of aesthetics on the popularity of online videos using the available meta-data about the internet audience’s appreciation. This can also provide a new tool for video content creators to assess their work and assist them in the production of content of higher aesthetic quality.
Keyword: computational aesthetics; skill transferability; video classification;
Cite as: Lemarchand, F. (2017). From computational aesthetic prediction for images to films and online videos [Special Issue]. AVANT, 8, 69–78. doi:10.26913/80s02017.0111.0007