@inproceedings{f424a517c98249309f2c9705860fef9b,
title = "Online Video Object Segmentation Based on Region and Edge Consistency",
abstract = "This paper proposes a novel online video object segmentation method by using region and edge consistency information. Motivated by the fact that the object edge information is similar between adjacent frames, we firstly generate object proposal based on region and edge consistency, which strengthens the confidence of regions nearby the obvious edges. Subsequently, we fuse optical flow into Markov Random Field (MRF) model to accurately segment moving object. Given an annotation frame for the initial object region, we can process video frames online within a short time. The experiments on benchmark video dataset demonstrate that the proposed method outperforms the existing state-of-the-art methods, especially in the scenarios with similar objects.",
keywords = "MRF, edge consistency., object segmentation, superpixel, supervoxelm",
author = "Jingjing Ma and Qingjie Zhao and Peng Lv and Mbelwa, \{Jimmy T.\} and Hao Liu and Jianwei Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 ; Conference date: 15-01-2018 Through 18-01-2018",
year = "2018",
month = may,
day = "25",
doi = "10.1109/BigComp.2018.00056",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "333--339",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018",
address = "United States",
}