TY - GEN
T1 - Towards Demographic-Based Photographic Aesthetics Prediction for Portraitures
AU - Kairanbay, Magzhan
AU - See, John
AU - Wong, Lai Kuan
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/1/13
Y1 - 2018/1/13
N2 - Do women look at aesthetics differently from men? Does cultural background have an influence over the perception of beauty? Has age have any role in this? Psychological and art studies reveal striking differences in perception among various demographical aspects. This warrants attention particularly with the rapid growth in automatic evaluation of photo aesthetics. In this research, we investigate the influences of demographic factors of photographer towards the aesthetic quality of portrait photos from the computational perspective. An extended version of the large-scale AVA dataset was created with the inclusion of the photographers’ demographic data such as location, age and gender. We trained several demographic-centric CNN models, which are then fused together as a single multi-demographic CNN model to learn aesthetic tendencies in a holistic manner. We demonstrate the efficacy of our model in achieving state-of-the-art performance in predicting portraiture aesthetics.
AB - Do women look at aesthetics differently from men? Does cultural background have an influence over the perception of beauty? Has age have any role in this? Psychological and art studies reveal striking differences in perception among various demographical aspects. This warrants attention particularly with the rapid growth in automatic evaluation of photo aesthetics. In this research, we investigate the influences of demographic factors of photographer towards the aesthetic quality of portrait photos from the computational perspective. An extended version of the large-scale AVA dataset was created with the inclusion of the photographers’ demographic data such as location, age and gender. We trained several demographic-centric CNN models, which are then fused together as a single multi-demographic CNN model to learn aesthetic tendencies in a holistic manner. We demonstrate the efficacy of our model in achieving state-of-the-art performance in predicting portraiture aesthetics.
UR - http://www.scopus.com/inward/record.url?scp=85042124550&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-73603-7_43
DO - 10.1007/978-3-319-73603-7_43
M3 - Conference contribution
AN - SCOPUS:85042124550
SN - 9783319736020
T3 - Lecture Notes in Computer Science
SP - 531
EP - 543
BT - MultiMedia Modeling. MMM 2018
PB - Springer
T2 - 24th International Conference on MultiMedia Modeling 2018
Y2 - 5 February 2018 through 7 February 2018
ER -