A Comparative Study of Image Retargeting

by Michael Rubinstein · Diego Gutierrez · Olga Sorkine · Ariel Shamir

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The numerous works on media retargeting call for a methodological approach for evaluating retargeting results. We present the first comprehensive perceptual study and analysis of image retargeting. First, we create a large benchmark of images and conduct a large scale user study to compare a representative number of state-of-theart retargeting methods. Second, we present analysis of the users’ responses, where we find that humans in general agree on the evaluation of the results and show that some retargeting methods are consistently more favorable than others. Third, we examine whether computational image distance metrics can predict human retargeting perception. We show that current measures used in this context are not necessarily consistent with human rankings, and demonstrate that better results can be achieved using image features that were not previously considered for this task. We also reveal specific qualities in retargeted media that are more important for viewers. The importance of our work lies in promoting better measures to assess and guide retargeting algorithms in the future. The full benchmark we collected, including all images, retargeted results, and the collected user data, are available to the research community for further investigation at http://people.csail.mit.edu/mrub/retargetme.