Abstract

Social networking on mobile devices has become a commonplace of everyday life. In addition, photo capturing process has become trivial due to the advances in mobile imaging. Hence people capture a lot of photos everyday and they want them to be visually-attractive. This has given rise to automated, one-touch enhancement tools. However, the inability of those tools to provide personalized and content-adaptive enhancement has paved way for machine-learned methods to do the same. The existing typical machine-learned methods heuristically (e.g. kNN-search) predict the enhancement parameters for a new image by relating the image to a set of similar training images. These heuristic methods need constant interaction with the training images which makes the parameter prediction sub-optimal and computationally expensive at test time which is undesired. This paper presents a novel approach to predicting the enhancement parameters given a new image using only its features, without using any training images. We propose to model the interaction between the image features and its corresponding enhancement parameters using the matrix factorization (MF) principles. We also propose a way to integrate the image features in the MF formulation. We show that our approach outperforms heuristic approaches as well as recent approaches in MF and structured prediction on synthetic as well as real-world data of image enhancement.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Other

OtherIEEE Winter Conference on Applications of Computer Vision, WACV 2016
CountryUnited States
CityLake Placid
Period3/7/163/10/16

Fingerprint

Image enhancement
Factorization
Heuristic methods
Mobile devices
Imaging techniques

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Chandakkar, P. S., & Li, B. (2016). A structured approach to predicting image enhancement parameters. In 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 [7477571] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2016.7477571

A structured approach to predicting image enhancement parameters. / Chandakkar, Parag Shridhar; Li, Baoxin.

2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7477571.

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

Chandakkar, PS & Li, B 2016, A structured approach to predicting image enhancement parameters. in 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016., 7477571, Institute of Electrical and Electronics Engineers Inc., IEEE Winter Conference on Applications of Computer Vision, WACV 2016, Lake Placid, United States, 3/7/16. https://doi.org/10.1109/WACV.2016.7477571
Chandakkar PS, Li B. A structured approach to predicting image enhancement parameters. In 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7477571 https://doi.org/10.1109/WACV.2016.7477571
Chandakkar, Parag Shridhar ; Li, Baoxin. / A structured approach to predicting image enhancement parameters. 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. Institute of Electrical and Electronics Engineers Inc., 2016.
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