@inproceedings{oh2017personalized,
author = {Oh, Tae-Hyun and Joo, Kyungdon and Joshi, Neel and Wang, Baoyuan and Kweon, In So and Kang, Sing Bing},
title = {Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning},
booktitle = {ICCV 2017},
year = {2017},
month = {May},
abstract = {Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts (such a tearing). To achieve this, we present a new technique that uses object recognition and semantic segmentation as part of an optimization method to automatically create cinemagraphs from videos that are both visually appealing and semantically meaningful. Given a scene with multiple objects, there are many cinemagraphs one could create. Our method evaluates these multiple candidates and presents the best one, as determined by a model trained to predict human preferences in a collaborative way. We demonstrate the effectiveness of our approach with multiple results and a user study.},
url = {http://approjects.co.za/?big=en-us/research/publication/personalized-cinemagraphs-using-semantic-understanding-and-collaborative-learning/},
}