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An Unsupervised Feature Selection Dynamic Mixture Model for Motion Segmentation | IEEE Journals & Magazine | IEEE Xplore

An Unsupervised Feature Selection Dynamic Mixture Model for Motion Segmentation


Abstract:

The automatic clustering of time-varying characteristics and phenomena in natural scenes has recently received great attention. While there exist many algorithms for moti...Show More

Abstract:

The automatic clustering of time-varying characteristics and phenomena in natural scenes has recently received great attention. While there exist many algorithms for motion segmentation, an important issue arising from these studies concerns that for which attributes of the data should be used to cluster phenomena with a certain repetitiveness in both space and time. It is difficult because there is no knowledge about the labels of the phenomena to guide the search. In this paper, we present a feature selection dynamic mixture model for motion segmentation. The advantage of our method is that it is intuitively appealing, avoiding any combinatorial search, and allowing us to prune the feature set. Numerical experiments on various phenomena are conducted. The performance of the proposed model is compared with that of other motion segmentation algorithms, demonstrating the robustness and accuracy of our method.
Published in: IEEE Transactions on Image Processing ( Volume: 23, Issue: 3, March 2014)
Page(s): 1210 - 1225
Date of Publication: 16 January 2014

ISSN Information:

PubMed ID: 24723523

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