Learning visual composition preferences from an annotated corpus generated through gameplay

Abstract

This paper describes a game called Panorama, designed to facilitate data collection to study visual composition preferences. Design considerations for Panorama, implementation of composition rules, and data collection for an experiment to learn individual and collective preferences is described. Images taken through gameplay in Panorama are automatically scored for composition quality and contribute to a corpus of domain-specific virtual photographs annotated by visual features and scores. Scores in Panorama represent rules of good composition from photography textbooks. In the current version, Panorama scores photographs along balance, thirds alignment, symmetry, and spacing dimensions. Pairwise preference rankings are collected on images from this corpus through crowd-sourcing. Results are presented from data on relative pairwise rankings on the images to learn individual as well as general composition preferences over features annotated in Panorama images. This work seeks to extend the ability of AI systems to learn and reason about high-level aesthetic features of photographs that could be utilized for various procedural camera control and aesthetic layout algorithms in video games.