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A multi-class classification strategy for Fisher scores: Application to signer independent sign language recognition
2010
Pattern Recognition
Fisher kernels combine the powers of discriminative and generative classifiers by mapping the variable-length sequences to a new fixed length feature space, called the Fisher score space. The mapping is based on a single generative model and the classifier is intrinsically binary. We propose a strategy that applies a multiclass classification on each Fisher score space and combines the decisions of multiclass classifiers. We experimentally show that the Fisher scores of one class provide
doi:10.1016/j.patcog.2009.12.002
fatcat:f4mpgsyadbbozi62czyuvwmxny