从小样本案例中的示例学习：面部表情识别。
Learning from examples in the small sample case: face expression recognition.
作者信息
Guo Guodong, Dyer Charles R
机构信息
Computer Sciences Department, University of Wisconsin-Madison, Madison, WI 53706, USA.
出版信息
IEEE Trans Syst Man Cybern B Cybern. 2005 Jun;35(3):477-88. doi: 10.1109/tsmcb.2005.846658.
Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear programming for both feature selection and classifier training is introduced. A pairwise framework for feature selection, instead of using all classes simultaneously, is presented. Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost. Finally, each algorithm is analyzed and a new categorization of these algorithms is given, especially for learning from examples in the small sample case.
当无法获得大量示例来表示每个模式或对象类别时，基于示例的计算机视觉学习可能会很困难。在这种情况下，从少量样本中学习具有实际价值。为了研究这个问题，我们考虑了在每个表情只有少量训练图像的情况下进行面部表情识别的任务。介绍了一种基于线性规划的新技术，用于特征选择和分类器训练。提出了一种用于特征选择的成对框架，而不是同时使用所有类别。实验结果将该方法与其他三种方法进行了比较：简化贝叶斯分类器、支持向量机和AdaBoost。最后，对每种算法进行了分析，并给出了这些算法的新分类，特别是对于小样本情况下的示例学习。
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