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Appearance-consistent Video Object Segmentation Based on a Multinomial Event Model
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DOI：10.1145/3321507.png)
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Abstract
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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.
Keywords:
Multinomial event model
appearance consistency
Markov random field
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