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In this paper, we try to combine Bag-of-Words (BoW) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) together for one kind of sparse representation in the seafloor visual saliency evaluation. Properties in the water, due to the large amount of acoustic noises, sonar signals are easily polluted and interfered during image collection, and the sonar images usually diverge from...
In this paper, a novel approach based on bag-of-words and compressed sensing has been proposed for the sparse representation of the underwater images. Properties in the water, such as the limited range, non-uniform lighting, low contrast, diminished colors, has been taken into consideration for underwater image sparse representation. Our method is a simple and computationally efficient extension of...
In this paper, we propose a novel method for fast face recognition called L1/2-regularized sparse representation using hierarchical feature selection. By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in....
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