This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models are constructed for target classification based on the AdaBoost cascade classification framework. A new function for updating sample weights has been designed in this paper to improve the accuracy of the classifier. And new iteration rules of classifier have been made to reduce the training time of the proposed method. The experimental results on the sonar dataset which are proposed for improving the generalization ability in this paper show that the classification accuracy of the proposed algorithm is about 92%, and the accuracy on Cifar-10 dataset is also higher than general methods.

中文翻译：

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一种用于声纳图像的基于 SVM 的 AdaBoost 级联分类器

本文针对低分辨率和噪声的声纳图像提出了一种改进的 AdaBoost 分类器。首先使用定向梯度直方图（HOG）进行特征提取，同时通过支持向量机（SVM）得到弱分类器。然后，基于 AdaBoost 级联分类框架构建多个 SVM 模型进行目标分类。为了提高分类器的准确率，本文设计了一个新的样本权重更新函数。并制定了新的分类器迭代规则，以减少所提出方法的训练时间。在本文提出的提高泛化能力的声纳数据集上的实验结果表明，该算法的分类准确率约为92%，