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Ordered smooth representation clustering
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DOI：10.1007/s13042-019-01018-0.png)
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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.
Keyword:
Subspace clustering
Smooth representation clustering
Kernel function
KNN graph
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