Name
Abstract | ||
|---|---|---|
In this study, we propose an effective and efficient algorithm for unconstrained video object segmentation, which is achieved in a Markov random field (MRF). In the MRF graph, each node is modeled as a superpixel and labeled as either foreground or background during the segmentation process. The unary potential is computed for each node by learning a transductive SVM classifier under supervision by a few labeled frames. The pairwise potential is used for the spatial-temporal smoothness. In addition, a high-order potential based on the multinomial event model is employed to enhance the appearance consistency throughout the frames. To minimize this intractable feature, we also introduce a more efficient technique that simply extends the original MRF structure. The proposed approach was evaluated in experiments with different measures and the results based on a benchmark demonstrated its effectiveness compared with other state-of-the-art algorithms.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3321507 | ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) |
Keywords | Field | DocType |
Markov random field, Multinomial event model, appearance consistency | Transduction (machine learning),Pairwise comparison,Unary operation,Pattern recognition,Segmentation,Markov random field,Computer science,Event model,Multinomial distribution,Computer network,Artificial intelligence,Smoothness | Journal |
Volume | Issue | ISSN |
15 | 2 | 1551-6857 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yadang Chen | 1 | 4 | 1.79 |
| Chuanyan Hao | 2 | 4 | 1.79 |
| Alex X. Liu | 3 | 2727 | 174.92 |
| Enhua Wu | 4 | 916 | 115.33 |