652Accesses
18Citations
Abstract
One key task in forensic science is to perform criminal investigation through image database retrieval. Of the various images, tire pattern is an important type of image data for crime scene investigation. However, different rotation and direction of tire patterns are often encountered and is insufficient to use the conventional multi-scale texture feature extraction method which is not rotational invariant. To alleviate this problem, the paper proposed two new texture feature extraction methods based on the Radon transform and Curvelet transform. The experiments were conducted using a tire pattern database containing 400 images. The results show that the proposed methods effectively overcome the influences of rotation and significantly improve the retrieval efficiency.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.












Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Cands, E. J. (1998).Ridgelets: Theory and applications. USA: Department of Statistics, Stanford University.
Cands, E. J. (1999).D L Donoho. Curvelets: Department of Statistics, Stanford University.
Deans, S. R. (1983).The Radon transform and some of its applications. New York: Wiley.
Donoho, D. L. (1998).Orthonormal ridgelets and linear singularities. USA: Department of Statistics, Stanford University.
Donoho, D. L., & Duncan, M. R. (2000). Digital Curvelet transform: Strategy, implementation and experiments.SPIE,4056, 12–29.
Kingsbury, N.G. (1998). The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement. InProceedings of European signal processing conference, 1998, pp. 319–322.
Kingsbury, N. G. (2000). A dual-tree complex wavelet transform with improved orthogonality and symmetry properties.IEEE International Conference on Image Processing,2, 375–378.
Kingsbury, N. G. (2000). Complex wavelets for shift invariant analysis and filtering of signals.Applied and Computational Harmonic Analysis,10(3), 234–253.
Kourosh, J. K., & Hamid, S. Z. (2005). Rotation-invariant multiresolution texture analysis using Radon and wavelet transform.IEEE Transactions on Image Processing,14(6), 783–794.
Licheng, Jiao, & Shan, Tan. (2003). Development and prospect of image multiscale geometric analysis.Acta Photonica Sinica,31(12A), 1975–1981.
Liu, Ying, Zhang, Dengsheng, & Lu, Guojun. (2008). A survey of content-based image retrieval with high level semantics.Pattern Recognition,40(1), 262–282.
Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(7), 674–692.
Patil, S., & Talbar, S. (2012). Multiresolution analysis using complex wavelet and Curvelet features for content based image retrieval.International Journal of Computer Applications,47(17), 6–10.
Vetterli, M. (2001). Wavelets, approximation and compression.IEEE Signal Processing Magazine,18(5), 59–73.
Zhiyong, A. & Zhiyong, Z. (2007). Content-based image image retrieval based on wavelet transform and radon transform. InProceeding of 2nd IEEE conference on industrial electronics and applications, 2007, pp. 1878–1881.
Zhiyong, An, Shan, Zhao, & Xiaohua, Wang. (2007). Content-based image retrieval based on the multi-scale radon transform.Acta Photonica Sinica,36(6), 1176–1180.
Zong, Li, Ying, Liu, & Daxiang, Li. (2013). A new texture feature extraction method for image retrieval. In2013 Fourth international conference on intelligent control and information processing, Beijing, China, pp. 482–486.
Acknowledgments
This work was supported by National Natural Science Foundation of China project No. 61202183 and Fund project of Shaanxi Province Education Office No. 12JK0504,as well as Shaanxi ‘100 Distinguished Experts’ Plan.
Author information
Authors and Affiliations
Center for Image and Information Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710061, People’s Republic of China
Ying Liu, Haoyang Yan & Keng-Pang Lim
- Ying Liu
Search author on:PubMed Google Scholar
- Haoyang Yan
Search author on:PubMed Google Scholar
- Keng-Pang Lim
Search author on:PubMed Google Scholar
Corresponding author
Correspondence toYing Liu.
Rights and permissions
About this article
Cite this article
Liu, Y., Yan, H. & Lim, KP. Study on rotation-invariant texture feature extraction for tire pattern retrieval.Multidim Syst Sign Process28, 757–770 (2017). https://doi.org/10.1007/s11045-015-0373-0
Received:
Revised:
Accepted:
Published:
Issue Date:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative