Personalization of image enhancement

Sing Bing Kang (MSR), Ashish Kapoor (MSR), Dani Lischinski (Hebrew U. of Jerusalem)

Abstract

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.

We would like to enhance images in the following ways:

Our system is as follows:

We use 25 training images with a “design gallery”-like interface to allow users to easily specify the best enhanced images:

If you wish to use our training images for your own research work, please contact Sing Bing Kang.

References:

· S.B. Kang, A. Kapoor, and D. Lischinski, "Personalization of image enhancement," IEEE Conf. on Computer Vision and Pattern Recognition, June 2010.

· J. Caicedo, A. Kapoor, and S.B. Kang, “Collaborative Personalization of Image Enhancement,” IEEE Conf. on Computer Vision and Pattern Recognition, June 2011 (followup work: using collaborative filtering to discover clusters in user preferences).

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