A multi-class classification strategy for Fisher scores: Application to signer independent sign language recognition

作者：Aran Oya^*; Akarun Lale

来源：Pattern Recognition, 2010, 43(5): 1776-1788.

DOI：10.1016/j.patcog.2009.12.002

• Fisher scores
• Multi-class classification
• Hidden Markov models
• Generative and discriminative classifiers
• Sign language 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 multi-class classification strategy that applies a multi-class classification on each Fisher score space and combines the decisions of multi-class classifiers We experimentally show that the Fisher scores of one class provide discriminative information for the other classes as well We compare several multi-class classification strategies for Fisher scores generated from the hidden Markov models of sign sequences The proposed multi-class classification strategy increases the classification accuracy in comparison with the state of the art strategies based on combining binary classifiers. To reduce the computational complexity of the Fisher score extraction and the training phases, we also propose a score space selection method and show that, similar or even higher accuracies can be obtained by using only a subset

• 出版日期2010-5