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Study on rotation-invariant texture feature extraction for tire pattern retrieval

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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.

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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.

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Authors and Affiliations

  1. 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

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  1. Ying Liu
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  2. Haoyang Yan
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  3. Keng-Pang Lim
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Correspondence to Ying Liu.

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Liu, Y., Yan, H. & Lim, KP. Study on rotation-invariant texture feature extraction for tire pattern retrieval. Multidim Syst Sign Process 28, 757–770 (2017). https://doi.org/10.1007/s11045-015-0373-0

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  • DOI: https://doi.org/10.1007/s11045-015-0373-0

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