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A novel multi-view nonparametric discriminant analysis method is proposed for the application of cross-modal image retrieval and zero-shot recognition. We exploit the class boundary structure and discrepancy information of the available views in order to formulate an optimization criterion, which is automatically adjusted to the multi-view class structures. The proposed method allows for multiple...
Traditional Artificial Neural Networks (ANNs) such as Multi-Layer Perceptrons (MLPs) and Radial Basis Functions (RBFs) were designed to simulate biological neural networks; however, they are based only loosely on biology and only provide a crude model. This in turn yields well-known limitations and drawbacks on the performance and robustness. In this paper we shall address them by introducing a novel...
In this paper a variant of the binary Support Vector Machine classifier that exploits intrinsic and penalty graphs in its optimization problem is proposed. We show that the proposed approach is equivalent to a two-step process where the data is firstly mapped to an optimal discriminant space of the input space and, subsequently, the original SVM classifier is applied. Our approach exploits the underlying...
In this paper, we present an unsharp masking-based approach for noise smoothing and edge enhancing. And apply it to color image processing. The proposed structure is similar to the conventional unsharp masking structure, however, a nonlinear function is added to control the behavior of the operator. The proposed scheme enhances the true details, limits the overshoot near sharp edges and attenuates...
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