Concept of self-reinforcing color enhancement. As more photos are enhanced by the user, the system implicitly and progressively learns the user's preferences and, as a result, the system is able to support the user in an increasingly effective manner.
A working prototype system, named SelPh. It has several user support functions enabled by the self-reinforcement, including enhanced sliders and confidence-based adaptation.
Abstract
Color enhancement is a very important aspect of photo editing. Even when photographers have tens of or hundreds of photographs, they must enhance each photo one by one by manually tweaking sliders in software such as brightness and contrast, because automatic color enhancement is not always satisfactory for them. To support this repetitive manual task, we present self-reinforcing color enhancement, where the system implicitly and progressively learns the user's preferences by training on their photo editing history. The more photos the user enhances, the more effectively the system supports the user. We present a working prototype system called SelPh, and then describe the algorithms used to perform the self-reinforcement. We conduct a user study to investigate how photographers would use a self-reinforcing system to enhance a collection of photos. The results indicate that the participants were satisfied with the proposed system and strongly agreed that the self-reinforcing approach is preferable to the traditional workflow.
Videos
Quick Preview Video (0:30)
Main Video (3:15)
Download
- Paper
- Main video
- Sub video (editing sequence using SelPh)
- Preview video
- Software (macOS)
- Source Code
- Slide
Publication
. 2016. SelPh: Progressive Learning and Support of Manual Photo Color Enhancement. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16), pp.2520--2532.
Slide/Presentation
Contact
Yuki Koyama - koyama@is.s.u-tokyo.ac.jp
News
- 2016.06.17 - Added the link for the presentation video recorded at CHI 2016.
- 2016.05.10 - Presented at CHI 2016.
- 2016.03.06 - Included in CHI 2016 Technical Program Preview.
- 2016.01.25 - Released the source codes and the software.
- 2016.01.22 - Opened this project page.
Links
- Yuki Koyama - The 1st author's personal web page.
- Daisuke Sakamoto - The 2nd author's personal web page.
- Takeo Igarashi - The last author's personal web page.
- Related Project (UIST 2014) - A related project published at UIST 2014, where the system learns aesthetic preferences using crowdsourcing, while SelPh learns aesthetic preferences from editing history.
Acknowledgments
Yuki Koyama is funded by JSPS research fellowship. This work was supported by JSPS KAKENHI Grant Number 26-8574, 26240027.