Relative Learning from Web Images for Content-adaptive Enhancement

Autor:	Chandakkar, Parag S., Tian, Qiongjie, Li, Baoxin
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: ICME 2015
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1704.01250 Zobrazit plný text záznamu View this record from Arxiv