A robust SVM classification framework using PSM for multi-class recognition
A robust SVM classification framework using PSM for multi-class recognition
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DOI：
10.1186/s13640-015-0061-x
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发表时间：
2015-03-11
影响因子：
2.4
通讯作者：
Ariki, Yasuo
中科院分区：
文献类型：
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作者：
Chen, Jinhui;Takiguchi, Tetsuya;Ariki, Yasuo
我们的研究集中在能够在不依赖大规模数据集的情况下快速准确地处理图像的分类器的问题，从而为面部表情识别(FER)和物体识别提供了一个健壮的分类框架。该框架基于支持向量机，并采用了三种关键方法来增强其稳健性。首先，使用扰动子空间方法(PSM)来扩展任务样本训练的样本空间范围，这是提高训练系统健壮性的有效途径。其次，该框架采用加速稳健特征(SURF)作为特征，更适合于处理实时情况。第三，引入区域属性对基于支持向量机的分类结果进行评价和修正。这样可以提高支持向量机的分类能力。将这些方法结合起来，提出的方法具有以下有益的贡献。首先，可以提高支持向量机的效率。实验表明，该方法能够有效地减少样本数量，从而显著减少训练时间。其次，识别精度可以与最先进的算法相媲美。第三，它的通用性很好，不仅可以应用于目标识别，还可以应用于FER。
Our research focuses on the question of classifiers that are capable of processing images rapidly and accurately without having to rely on a large-scale dataset, thus presenting a robust classification framework for both facial expression recognition (FER) and object recognition. The framework is based on support vector machines (SVMs) and employs three key approaches to enhance its robustness. First, it uses the perturbed subspace method (PSM) to extend the range of sample space for task sample training, which is an effective way to improve the robustness of a training system. Second, the framework adopts Speeded Up Robust Features (SURF) as features, which is more suitable for dealing with real-time situations. Third, it introduces region attributes to evaluate and revise the classification results based on SVMs. In this way, the classifying ability of SVMs can be improved. Combining these approaches, the proposed method has the following beneficial contributions. First, the efficiency of SVMs can be improved. Experiments show that the proposed approach is capable of reducing the number of samples effectively, resulting in an obvious reduction in training time. Second, the recognition accuracy is comparable to that of state-of-the-art algorithms. Third, its versatility is excellent, allowing it to be applied not only to object recognition but also FER.