基于支持向量机的舰船图像识别

支持向量机(SVM)是一种基于超平面分类的新的学习方法，具有很强的泛化能力。研究了支持向量机的学习机理，以及实现支持向量机的序贯最小优化算法(SMO)，并用来对舰船图像进行识别。首先将待识别目标进行二维小波分解，获取不同尺度下的小波系数，然后对其进行主元分析，得到的主元分量作为支持向量机的特征量输入。实验结果表明，该方法具有良好的分类性能。

Warship Image Recognition Based on Support Vector Machine

Support Vector Machine (SVM) is a new learning method based on hyperplane classification, and its powerful in common use. This paper describes an application of SVM on warship recognition. After the learning mechanism of SVM and the sequential minimal optimization method are analyzed, a warship recognition system is proposed, which uses 2-D Haar wavelet to decompose the image, and get the wavelet coefficients, then calculate principal components which will be the inputs of SVM. Experiment results indicate that this method has good performance and robustness.