A holistic approach to aesthetic enhancement of photographs

Abstract

This article presents an interactive application that enables users to improve the visual aesthetics of their digital photographs using several novel spatial recompositing techniques. This work differs from earlier efforts in two important aspects: (1) it focuses on both photo quality assessment and improvement in an integrated fashion, (2) it enables the user to make informed decisions about improving the composition of a photograph. The tool facilitates interactive selection of one or more than one foreground objects present in a given composition, and the system presents recommendations for where it can be relocated in a manner that optimizes a learned aesthetic metric while obeying semantic constraints. For photographic compositions that lack a distinct foreground object, the tool provides the user with crop or expansion recommendations that improve the aesthetic appeal by equalizing the distribution of visual weights between semantically different regions. The recomposition techniques presented in the article emphasize learning support vector regression models that capture visual aesthetics from user data and seek to optimize this metric iteratively to increase the image appeal. The tool demonstrates promising aesthetic assessment and enhancement results on variety of images and provides insightful directions towards future research.