@inproceedings{ae6480802e75454bb8bec7711133d90d,
title = "Geodesic distance histogram feature for video segmentation",
abstract = "This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets.",
author = "Hieu Le and Vu Nguyen and Yu, \{Chen Ping\} and Dimitris Samaras",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 13th Asian Conference on Computer Vision, ACCV 2016 ; Conference date: 20-11-2016 Through 24-11-2016",
year = "2017",
doi = "10.1007/978-3-319-54181-5\_18",
language = "English",
isbn = "9783319541808",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "275--290",
editor = "Yoichi Sato and Ko Nishino and Vincent Lepetit and Shang-Hong Lai",
booktitle = "Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers",
}