Personalization of Image Enhancement

  • Sing Bing Kang ,
  • Ashish Kapoor ,
  • Dani Lischinski

2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |

Published by IEEE


We address the problem of incorporating user preference in automatic image enhancement. Unlike generic tools for automatically enhancing images, we seek to develop methods that can first observe user preferences on a training set, and then learn a model of these preferences to personalize enhancement of unseen images. The challenge of designing such system lies at intersection of computer vision, learning, and usability; we use techniques such as active sensor selection and distance metric learning in order to solve the problem. The experimental evaluation based on user studies indicates that different users do have different preferences in image enhancement, which suggests that personalization can further help improve the subjective quality of generic image enhancements.