- Title
- A Rotation invariant HOG descriptor for tire pattern image classification
- Creator
- Liu, Ying; Ge, Yuxiang; Wang, Fuping; Liu, Qiqi; Lei, Yanbo; Zhang, Dengsheng; Lu, Guojun
- Date
- 2019
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180903
- Identifier
- vital:15824
- Identifier
-
https://doi.org/10.1109/ICASSP.2019.8683689
- Identifier
- ISBN:978-1-4799-8131-1
- 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.
- Publisher
- IEEE
- Relation
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Brighton, UK, 12-17 May 2019. p. 2412-2416
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright IEEE
- Subject
- Dominant gradient; Feature extraction; Histogram of gradient; Histograms; Information processing; Lighting; Rotation-invariant; Standards; Subspace constraints; Texture feature; Tire pattern classification; Tires
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