Beauty is in the eye of the beholder: Demographically oriented analysis of aesthetics in photographs

Magzhan Kairanbay, John See, Laikuan Wong

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Aesthetics is a subjective concept that is likely to be perceived differently among people of different ages, genders, and cultural backgrounds. While techniques that directly compute this concept in images has seen increasing attention by the multimedia and machine-learning community, there are very few attempts at encoding the influences from the photographer's viewpoint. This work demonstrates how the aesthetic quality of photos can be better learned by accounting for the demographic background of a photographer. A new AVA-PD (Photographer Demographic) dataset is created to supplement the AVA dataset by providing photographers' age, gender and location attributes. Two deep convolutional neural network (CNN) architectures are proposed to utilize demographic information for aesthetic prediction of photos; both are shown to yield better prediction capabilities compared to most existing approaches. By leveraging on AVA-PD meta-data, we also present some additional machine-learnable tasks such as identifying the photographer and predicting photography styles from a person's gallery of photos.

Original languageEnglish
Article number63
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume15
Issue number2s
Early online date25 Jul 2019
DOIs
Publication statusPublished - Aug 2019

Keywords

  • AVA
  • Convolutional neural networks
  • Demographic attributes
  • Image aesthetic evaluation
  • Photographer demographics

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications

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