Abstract
In this paper, we develop an interactive Non-negative Matrix Factorization method for representative action video discovery. The original video is first evenly segmented into some short clips and the bag-of-words model is used to describe each clip. Then a temporal consistent Non-negative Matrix Factorization model is used for clustering and action segmentation. Since the clustering and segmentation results may not satisfy the user’s intention, two extra human operations: MERGE and ADD are developed to permit user to improve the results. The newly developed interactive Non-negative Matrix Factorization method can therefore generate personalized results. Experimental results on the public Weizman dataset demonstrate that our approach is able to improve the action discovery and segmentation results.
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References
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© 2015 Springer International Publishing Switzerland
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Teng, H., Liu, H., Yu, L., Sun, F. (2015). Representative Video Action Discovery Using Interactive Non-negative Matrix Factorization. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_23
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DOI: https://doi.org/10.1007/978-3-319-25393-0_23
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-25393-0
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