We formulate image segmentation as subspace clustering of image feature vectors. We propose a subspace representation model using a nonconvex extension of trace Lasso and a nonconvex approximation of rank function to regularize the subspace representation. The proposed model can adaptively capture the local and the global structures of the subspace representation so that the subspace representation can reveal the real subspace structure of the data and obtains excellent clustering performance. Experimental results show that the proposed model is better than the previous models in clustering and natural image segmentation.
"Image segmentation by adaptive nonconvex local and global subspace representation," Journal of Electronic Imaging 25(3), 033026 (24 June 2016). https://doi.org/10.1117/1.JEI.25.3.033026