TY - GEN
T1 - A structured approach to predicting image enhancement parameters
AU - Chandakkar, Parag Shridhar
AU - Li, Baoxin
N1 - Funding Information: The work was supported in part by an ARO grant (#W911NF1410371) and an ONR grant (#N00014-15-1-2344). Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of ARO or ONR. Publisher Copyright: © 2016 IEEE.
PY - 2016/5/23
Y1 - 2016/5/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84977639950&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977639950&partnerID=8YFLogxK
U2 - 10.1109/WACV.2016.7477571
DO - 10.1109/WACV.2016.7477571
M3 - Conference contribution
T3 - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
BT - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Y2 - 7 March 2016 through 10 March 2016
ER -