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One sample per person (OSPP) face recognition is a challenging problem in face recognition community. Lack of samples is the main reason for the failure of most algorithms in OSPP. In this study, the authors propose a new algorithm to generalise intra-class variations of multi-sample subjects to single-sample subjects by deep autoencoder and reconstruct new samples. In the proposed algorithm, a generalised...
One sample per person face recognition (OSPP) is a challenging problem in face recognition community. Lack of samples leads to performance deterioration. Extended sparse representation-based classifier (ESRC) demonstrates excellent performance on OSPP. However, because there are intra-class variant atoms in the dictionary of ESRC, the number of atoms in the dictionary is always large and it will spend...
Remote sensing classification is the core of converting satellite image to useful geographic information. Many methods have been proposed for improving classification accuracy, however, the results are always dissatisfied. The reason is that there is serious spectral overlay phenomenon between classes which decrease the classification accuracy. This paper introduced an evidential reasoning "soft"...
Artificial neural network (ANN) is an important part of artificial intelligence, it has been widely used in remote sensing classification research field. Wetlands remote sensing classification based on ANN is difficult, because of the complex feature of wetlands areas. The purity of training samples for remote sensing image supervised classification is difficult to guarantee that will affect the classification...
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