Aesthetic Photo Enhancement using Machine Learning and Case-Based Reasoning

Joachim Folz, Christian Schulze, Damian Borth, Andreas Dengel
Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia, Pages 27-32, Brisbane, Queensland, Australia, ACM, ACM, New York, NY, USA, New York, NY, USA, 10/2015


Broad availability of camera devices allows users to easily create, upload, and share photos on the Internet. However, users not only want to share their photos in the very moment they acquire them, but also ask for tools to enhance the aesthetics of a photo before upload as seen by the popularity of services such as Instagram. This paper presents a semi-automatic assistant system for aesthetic photo enhancement. Our system employs a combination of machine learning and case-based reasoning techniques to provide a set of operations (contrast, brightness, color, and gamma) customized for each photo individually. The inference is based on scenery concept detection to identify enhancement potential in photos and a database of sample pictures edited by desktop publishing experts to achieve a certain look and feel. Capabilities of the presented system for instant photo enhancements were confirmed in a user study with twelve subjects indicating a clear preference over a traditional photo enhancement system, which required more time to handle and provided less satisfying results. Additionally, we demonstrate the benefit of our system in an online demo.



@inproceedings{ FOLZ2015,
	Title = {Aesthetic Photo Enhancement using Machine Learning and Case-Based Reasoning},
	Author = {Joachim Folz and Christian Schulze and Damian Borth and Andreas Dengel},
	BookTitle = {Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia},
	Month = {10},
	Year = {2015},
	Publisher = {ACM},
	Pages = {27-32},
	Organization = {ACM},
	Address = {New York, NY, USA}

Last modified:: 30.08.2016