An SVM-Based AdaBoost Cascade Classifier for Sonar Image

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.