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Ordered smooth representation clustering

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Abstract

The smooth representation (SMR) model is a widely used segmentation method in computer vision. This model adopts the K nearest neighbour (KNN) graph to select samples for representation. All neighbours in the KNN graph are assumed to be equally important candidates. In this paper, we use special weights that are calculated by a novel cross-view kernel function to evaluate the contributions of neighbours to the subspace clustering in SMR. The neighbours that are found by the Gaussian similarity formula can be considered long-range similar neighbours. We add another item to accurately reflect the order relation in the cross-view kernel function. This addition allows the kernel function to generalize the conventional SMR method for sequential data. The ordered smooth representation (OSMR) model outperforms other representative space clustering methods on public datasets, namely, the UCI database, the USPS database, Yale B datasets, the Freiburg–Berkeley Motion Segmentation database and a real-world mobile video that was captured by a smart phone.

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Acknowledgements

This research work is supported by the Chinese National Natural Science Foundation under Grant Nos. 61976053, 61772134, 61672157, U1805263 and 41601477, Fujian Province Natural Science Foundation under Grant No. 2018J01776 and the Leading Project in Science and Technology Department of Fujian Province under Grant No. 2015Y0054. We would like to express our thanks to the anonymous reviewers for their valuable comments and suggestions.

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  1. School of Mathematics and Informatics, Fujian Normal University, Fuzhou, China

    Liping Chen & Gongde Guo

  2. Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fuzhou, China

    Liping Chen & Gongde Guo

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  1. Liping Chen
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  2. Gongde Guo
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Correspondence to Gongde Guo.

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Chen, L., Guo, G. Ordered smooth representation clustering. Int. J. Mach. Learn. & Cyber. 10, 3301–3311 (2019). https://doi.org/10.1007/s13042-019-01018-0

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