NDLI: Aesthetic Photo Enhancement using Machine Learning and Case-Based Reasoning

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Aesthetic Photo Enhancement using Machine Learning and Case-Based Reasoning

Content Provider	ACM Digital Library
Author	Schulze, Christian Folz, Joachim Borth, Damian Dengel, Andreas
Abstract	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.
Starting Page	27
Ending Page	32
Page Count	6
File Format	PDF
ISBN	9781450337502
DOI	10.1145/2813524.2813531
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Photographic problem detection Image processing Enhancement potential detection Case-based reasoning Machine learning Photo enhancement Aesthetics Desktop publishing
Content Type	Text
Resource Type	Article

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