Open AccessGarbage image recognition and classification based on hog feature and SVM-Boosting
Author(s) -
Weifeng Wang,
Zhang Baobao,
Zhiqiang Wang,
Zhang FangZhi,
Qiang Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1966/1/012002
Subject(s) - support vector machine , boosting (machine learning) , artificial intelligence , pattern recognition (psychology) , computer science , contextual image classification , garbage , feature (linguistics) , image (mathematics) , statistical classification , feature extraction , machine learning , data mining , linguistics , philosophy , programming language
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.