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This paper presents a new technique for hyperspectral images classification based on simultaneous sparse approximation. The proposed approach consists in formulating the problem as a convex multi-objective optimization problem which incorporates a term favoring the simultaneous sparsity of the estimated coefficients and a term enforcing a regularity constraint along the rows of the coefficient matrix...
This paper presents a novel similarity measure in a texture retrieval framework based on statistical modeling in wavelet domain. In this context, we use the recently proposed finite mixture of generalized Gaussian distribution (MoGG) thanks to its ability to model accurately a wide range of wavelet sub-bands histograms. This model has already been relied on the approximation of Kullback–Leibler divergence...
This paper presents a new technique of simultaneous sparse approximation incorporating a regularity constraint along the coefficients matrix rows. This approach is decomposed in two steps: first a sparse representation of the coefficients matrix is obtained using a simultaneous greedy method. Then, a ℓ1 penalty regularization on the derivative of nonzero coefficients enforces a piecewise constant...
This paper extends greedy methods to simultaneous sparse approximation. This problem consists in finding good estimation of several input signals at once, using different linear combinations of a few elementary signals, drawn from a fixed collection. The sparse algorithms for which simultaneous versions are proposed are namely CoSaMP, OLS and SBR. These approaches are compared to Tropp's S-OMP algorithm...
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