NDLI: Relative learning from web images for content-adaptive enhancement

Content Provider	IEEE Xplore Digital Library
Author	Chandakkar, P.S. Qiongjie Tian Baoxin Li
Copyright Year	2015
Description	Author affiliation: Sch. of Comput., Inf. & Decision Syst. Eng., Arizona State Univ., Tempe, AZ, USA (Chandakkar, P.S.; Qiongjie Tian; Baoxin Li)
Abstract	Personalized and content-adaptive image enhancement can find many applications in the age of social media and mobile computing. This paper presents a relative-learning-based approach, which, unlike previous methods, does not require matching original and enhanced images for training. This allows the use of massive online photo collections to train a ranking model for improved enhancement. We first propose a multi-level ranking model, which is learned from only relatively-labeled inputs that are automatically crawled. Then we design a novel parameter sampling scheme under this model to generate the desired enhancement parameters for a new image. For evaluation, we first verify the effectiveness and the generalization abilities of our approach, using images that have been enhanced/labeled by experts. Then we carry out subjective tests, which show that users prefer images enhanced by our approach over other existing methods.
Starting Page	1
Ending Page	6
File Size	3521205
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177502
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brightness Visualization Image color analysis Databases Image enhancement Histograms Mathematical model subjective evaluation testing Content-adaptive image enhancement learning-to-rank
Content Type	Text
Resource Type	Article

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