Name
Abstract | ||
|---|---|---|
We propose an effective approach to make segmentation for objects in videos with an initial input of the object masks in a few frames of the source video. In this method, we cast the segmentation task as a Markov Random Field (MRF) labeling problem. Different from the conventional MRF models, our model uses an additional term of higher-order potential to better propagate the global consistency among frames. The higher-order potential presented in this paper is significant for the proposed method because of its capability to keep the long-range consistency during segmentation. In order to make the MRF energy minimized, we also introduce a smart skill that makes the intractable higher-order potential “invisible” during the optimization so that the problem can be solved simply by applying a standard graph cut algorithm. Besides, the entire process is operated in a bilateral space, where the labeling can be inferred efficiently on the vertices that are sampled regularly from the bilateral grid. The results of a comparison of our method with a number of recently developed methods show that it performs favorably against state-of-the-art algorithms on multiple benchmark data sets in view of accuracy and achieves a much faster runtime performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.03.020 | Neurocomputing |
Keywords | DocType | Volume |
Video,Object segmentation,Markov random field,Higher-order,Bilateral space | Journal | 401 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.38 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuanyan Hao | 1 | 4 | 1.79 |
| Yadang Chen | 2 | 4 | 1.79 |
| Zhixin Yang | 3 | 118 | 23.12 |
| Enhua Wu | 4 | 916 | 115.33 |