Garbage image recognition and classification based on hog feature and SVM-Boosting

Abstract

Due to the diversity of garbage types in our daily life, we will encounter many difficulties in the process of classification. In this regard, I combine hog features and boosting algorithm to develop a SVM classification method. Firstly, the input image is preprocessed to make the image more recognizable. Secondly, the hog algorithm is used to extract the features of the image. Finally, the classification device is trained, and the relevant information is sent to the image set. On this basis, the classification situation is detected. The final results show that the classification efficiency of the algorithm is as high as 95% or even more, which is about 10% higher than that of single SVM classification method. It can accurately classify garbage and has certain feasibility.