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Many high-dimensional data in computer vision essentially lie in multiple low-dimensional subspaces. Recently developed subspace clustering methods have shown good effectiveness in recovering the underlying low-dimensional subspace structure of high-dimensional data. The state-of-the-art methods show that sparseness and grouping effect of the affinity matrix are important for subspace clustering....
Image segmentation aims to partition an image into several disjoint regions with each region corresponding to a visual meaningful object. It is a fundamental problem in image processing and computer vision. Recently, subspace clustering methods shows great potential in image segmentation. In this work we formulate image segmentation as subspace clustering of image feature vectors. To extend the capture...
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