TY - GEN
T1 - SmartEye
T2 - 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019
AU - Ma, Shuai
AU - Wei, Zijun
AU - Tian, Feng
AU - Fan, Xiangmin
AU - Zhang, Jianming
AU - Shen, Xiaohui
AU - Lin, Zhe
AU - Huang, Jin
AU - Měch, Radomír
AU - Samaras, Dimitris
AU - Wang, Hongan
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/5/2
Y1 - 2019/5/2
N2 - Instant photo taking and sharing has become one of the most popular forms of social networking. However, taking high-quality photos is difficult as it requires knowledge and skill in photography that most non-expert users lack. In this paper we present SmartEye, a novel mobile system to help users take photos with good compositions in-situ. The back-end of SmartEye integrates the View Proposal Network (VPN), a deep learning based model that outputs composition suggestions in real time, and a novel, interactively updated module (P-Module) that adjusts the VPN outputs to account for personalized composition preferences. We also design a novel interface with functions at the front-end to enable real-time and informative interactions for photo taking. We conduct two user studies to investigate SmartEye qualitatively and quantitatively. Results show that SmartEye effectively models and predicts personalized composition preferences, provides instant high-quality compositions in-situ, and outperforms the non-personalized systems significantly.
AB - Instant photo taking and sharing has become one of the most popular forms of social networking. However, taking high-quality photos is difficult as it requires knowledge and skill in photography that most non-expert users lack. In this paper we present SmartEye, a novel mobile system to help users take photos with good compositions in-situ. The back-end of SmartEye integrates the View Proposal Network (VPN), a deep learning based model that outputs composition suggestions in real time, and a novel, interactively updated module (P-Module) that adjusts the VPN outputs to account for personalized composition preferences. We also design a novel interface with functions at the front-end to enable real-time and informative interactions for photo taking. We conduct two user studies to investigate SmartEye qualitatively and quantitatively. Results show that SmartEye effectively models and predicts personalized composition preferences, provides instant high-quality compositions in-situ, and outperforms the non-personalized systems significantly.
KW - Deep learning
KW - Interactive feedback
KW - Photo composition
KW - User preference modeling
UR - https://www.scopus.com/pages/publications/85067598842
U2 - 10.1145/3290605.3300701
DO - 10.1145/3290605.3300701
M3 - Conference contribution
AN - SCOPUS:85067598842
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 4 May 2019 through 9 May 2019
ER -