Multi-class classification strategies for Fisher scores of gesture and sign sequences
Multi-class classification strategies for Fisher scores of gesture and sign sequences
复制标题
手势和符号序列 Fisher 分数的多类分类策略
DOI：
10.1109/icpr.2008.4761774
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
L. Akarun
中科院分区：
文献类型：
--
作者：
O. Aran;L. Akarun
在本文中，我们提出了一种基于Fisher核的多类分类策略。Fisher核通过将变长序列映射到一个新的定长特征空间，结合了判别式和生成式分类器的能力。映射基于单一的生成模型，并且分类器本质上是二进制的。我们在每个Fisher分数空间上应用多类分类，而不是二类分类，并结合多类分类器的决策。通过对手势和手势序列的实验表明，从一个类的隐马尔可夫模型中提取的Fisher分数也为其他类提供了区分信息。与其他方法的比较表明，该方法最大限度地提高了基分类器的性能。
In this work, we propose a multi-class classification strategy based on Fisher kernels. Fisher kernels combine the powers of discriminative and generative classifiers by mapping variable-length sequences to a new fixed length feature space. The mapping is based on a single generative model and the classifier is intrinsically binary. We apply a multi-class classification, instead of a binary classification, on each Fisher score space and combine the decisions of multi-class classifiers. We show, through experiments on gesture and sign sequences, that the Fisher scores extracted from the HMM of one class provide discriminative information for other classes as well. Comparisons with other strategies show that the proposed method enhances the performance of the base classifier the most.