Context-Based Photography Learning using Crowdsourced Images and Social Media
Yogesh Singh Rawat, Mohan Kankanhalli · 2014
This paper presents a photography model based on machine learning which utilizes crowd-sourced images along with social media cues. As scene composition and camera parameters play a vital role in aesthetics of a captured image, the proposed system addresses the problem of learning photographic composition and camera parameters. Further, we observe that context is an important factor from a photography perspective, we therefore augment the learning with associated contextual information. We define context features based on factors such as time, geo-location, environmental conditions and type of image, which have an impact on photography. The meta information available with crowd-sourced images is utilized for context identification and social media cues are used for photo quality evaluation. We also propose the idea of computing the photographic composition basis, eigenrules and baserules, to support our composition learning method. The trained photography model can provide assistance to the user in determining image composition and camera parameters.