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- W2622329449 abstract "In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods-highlight-inhibition function, context-based saliency weighting, and the graph cut-are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method." @default.
- W2622329449 created "2017-06-15" @default.
- W2622329449 creator A5027377595 @default.
- W2622329449 creator A5059001271 @default.
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- W2622329449 date "2017-08-01" @default.
- W2622329449 modified "2023-10-16" @default.
- W2622329449 title "Reprint of “Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency”" @default.
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- W2622329449 doi "https://doi.org/10.1016/j.neunet.2017.06.001" @default.
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