Relative learning from web images for content-adaptive enhancement

Parag Shridhar Chandakkar, Qiongjie Tian, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Multimedia and Expo
PublisherIEEE Computer Society
ISBN (Print)9781479970827
StatePublished - Aug 4 2015
EventIEEE International Conference on Multimedia and Expo, ICME 2015 - Turin, Italy
Duration: Jun 29 2015Jul 3 2015


OtherIEEE International Conference on Multimedia and Expo, ICME 2015


  • Content-adaptive image enhancement
  • learning-to-rank
  • subjective evaluation testing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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