A Rotation Invariant HOG Descriptor for Tire Pattern Image Classification
Ying Liu, Yuxiang Ge, Fuping Wang, Qiqi Liu, Yanbo Lei, Dengsheng Zhang, Guojun Lu
Abstract
Texture feature is important in describing tire pattern image which provides useful clue in solving crime cases and traffic accidents. In this paper, we propose a novel texture feature extraction method based on HOG (Histogram of Oriented Gradient) and dominant gradient (DG) in tire pattern images, named HOG-DG. The proposed HOG-DG is not only robust to illumination and scale changes but also is rotation-invariant. In the proposed HOG-DG, HOG features are first computed from circular local cells, and HOG features from an image are concatenated and normalized using the DG to construct the HOG-DG feature. HOG-DG is used to train a support-vector-machine (SVM) classifier for tire pattern classification. Experimental results demonstrate its outstanding performance for tire pattern description.
BibTeX
@inproceedings{icassp2019_arotationinvaria,
title = {A Rotation Invariant HOG Descriptor for Tire Pattern Image Classification},
author = {Ying Liu and Yuxiang Ge and Fuping Wang and Qiqi Liu and Yanbo Lei and Dengsheng Zhang and Guojun Lu},
booktitle = {ICASSP 2019},
year = {2019}
}