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Hyperspectral imagery typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image; however, when used in statistical pattern-classification tasks, the resulting high-dimensional feature spaces often tend to result in ill-conditioned formulations. Popular dimensionality selection techniques such as filtering and wrapper methods fail...
Current image matting approaches are often implemented based upon color samples under various local assumptions. In this letter, a novel image matting algorithm is investigated by treating the alpha matting as a regression problem. Specifically, we learn spatially-varying relations between pixel features and alpha values using support vector regression. Via the learning-based approach, limitations...
This paper proposes a simple and efficient detection framework that uses reduced-set kernels. We first describe our approach which reduces the number of kernels. A convex optimization method is used for calculating the reduced sets. Following this, we propose a method that optimally designs the cascade. Our experimental results indicate that our method minimizes complexity regarding the number of...
For accelerating the training speed of support vector machines (SVM), a novel ldquomulti-trifurcate cascade (MTC)rdquo architecture was proposed in this paper, which held the advantages of fast feedback, high utilization rate of nodes, and more feedback support vectors. Then, a parallel algorithm for training SVM was designed based on the MTC architecture, and it was proven to converge to the optimal...
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