Abstract
This paper proposes a novel temporal data segmentation approach based on a graph partition model. To find the optimal segmentation, which maintains maximal connectivity within the same segment while keeping minimum association between different ones, we adopt the min-max cut as an objective function. For temporal data, a linear time algorithm is designed by importing the temporal constraints. With multi-pair comparison strategy, the proposed method is more robust than the existing pair-wise comparison ones. The experiments on TRECVID benchmarking platform demonstrate the effectiveness of our approach.
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Yuan, J., Zhang, B., Lin, F. (2005). Graph Partition Model for Robust Temporal Data Segmentation. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_88
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DOI: https://doi.org/10.1007/11430919_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
eBook Packages: Computer ScienceComputer Science (R0)