Cyganek Bogusław
AGH University of Science and Technology, Al. Mickiewicza 30, Kraków, Poland.
Int J Neural Syst. 2008 Aug;18(4):339-45. doi: 10.1142/S0129065708001646.
In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.
在本文中，我们提出了一种高效的颜色分割方法，该方法基于以单类模式运行的支持向量机分类器。尽管该方法可用于其他应用，但它是专门为道路标志识别系统开发的。所提方法的主要优点在于，特征颜色的分割不是在原始空间而是在高维特征空间中进行。通过这种方式，通常可以用线性超球体实现更好的数据封装。此外，分类器并不试图捕捉输入数据的整个分布，而这往往难以实现。相反，选择了称为支持向量的特征数据样本，这些样本允许构建包围大多数输入数据的最紧密超球体。然后，对测试数据的分类简单地包括测量其到找到的超球体中心的距离。实验结果表明了所提方法的高精度和高速度。
