Learning the Change for Automatic Image Cropping
Refereed conference paper presented and published in conference proceedings

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AbstractImage cropping is a common operation used to improve the visual quality of photographs. In this paper, we present an automatic cropping technique that accounts for the two primary considerations of people when they crop: removal of distracting content, and enhancement of overall composition. Our approach utilizes a large training set consisting of photos before and after cropping by expert photographers to learn how to evaluate these two factors in a crop. In contrast to the many methods that exist for general assessment of image quality, ours specifically examines differences between the original and cropped photo in solving for the crop parameters. To this end, several novel image features are proposed to model the changes in image content and composition when a crop is applied. Our experiments demonstrate improvements of our method over recent cropping algorithms on a broad range of images.
All Author(s) ListYan JZ, Lin S, Kang SB, Tang XO
Name of Conference26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Start Date of Conference23/06/2013
End Date of Conference28/06/2013
Place of ConferencePortland
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by Gerard Medioni, Ramin Zabih,
Pages971 - 978
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-27-10 at 00:55