A Consensus Model for Motion Segmentation in Dynamic Scenes

The study of phenomena segmentation in natural scenes has attracted growing attention and is a popular research topic. While there are many studies detailing algorithms for motion segmentation in dynamic scenes, an important question arising from these studies is how to combine these algorithms. How...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2016-12, Vol.26 (12), p.2240-2249
Main Authors: Thanh Minh Nguyen, Wu, Qingming Jonathan
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Computer vision
Consensus model
dynamic texture segmentation
Image segmentation
Mixture models
Motion segmentation
Questions
Robustness (mathematics)
Scene analysis
Unsupervised learning
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Summary:The study of phenomena segmentation in natural scenes has attracted growing attention and is a popular research topic. While there are many studies detailing algorithms for motion segmentation in dynamic scenes, an important question arising from these studies is how to combine these algorithms. How can the label correspondence problem be resolved? Answering this question is difficult, because there are no labeled training data available in clustering to guide the search. Also, different algorithms produce incompatible data labels resulting in intractable correspondence problems. This paper presents a new consensus model for motion segmentation in dynamic scenes, which aims to combine several unsupervised methods to achieve a more reliable and accurate result. The advantage of our method is that it is intuitively appealing. Numerical experiments on various phenomena are conducted. The performance of the proposed model is compared with the best state-of-the-art motion segmentation methods recently proposed in the literature, demonstrating the robustness and accuracy of our method.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2015.2511479