A structured approach to predicting image enhancement parameters

Parag Shridhar Chandakkar, Baoxin Li
2016 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)  
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 contentadaptive enhancement has paved way for machine-learned methods to do the same. The existing
more » ... l machinelearned 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.
doi:10.1109/wacv.2016.7477571 dblp:conf/wacv/ChandakkarL16 fatcat:b6z2zb7muzfrlnl5kp6rt3jbri