- Title
- Integrating line weber local descriptor and deep feature for tire indentation mark image classification
- Creator
- Liu, Ying; Che, Xin; Dong, Haitao; Li, Daxiang; Teng, Shyh; Lu, Guojun
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189034
- Identifier
- vital:17377
- Identifier
-
https://doi.org/10.1145/3488933.3488948
- Identifier
- ISBN:9781450384087 (ISBN)
- Abstract
- Tire indentation mark matching is an essential tool used for the investigation of criminal cases and traffic incidents. As such images are unique and uncommon, there is a lack of dedicated databases and relevant research on this topic. This paper presents a feature extraction algorithm effective for tire indentation mark image description. The main contributions include: (1) Line feature Weber local descriptor (LWLD) is proposed, which uses the Gabor orientations instead of the original gradient orientation. This feature can describe texture information of tire indentation mark image more efficiently. (2) An attention model is constructed to produce attention feature map of tire indentation mark image. This attention feature map is then fused with LWLD resulting in a feature with more powerful representation capability. Experimental results prove that the combined use of LWLD and attention model greatly enhances the performance of tire indentation mark image matching tasks. © 2021 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- 4th International Conference on Artificial Intelligence and Pattern Recognition, 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021,Virtual, Online,17-19 September 2021, 2021, ACM International Conference Proceeding Series p. 56-61
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 ACM
- Subject
- Attention model; Convolutional neural network; Feature fusion; Tire indentation mark image classification; Weber local descriptors
- Reviewed
- Funder
- National Natural Science Foundation of China, NSFC
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