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Information 2019, 10(2), 45; https://doi.org/10.3390/info10020045

Crime Scene Shoeprint Retrieval Using Hybrid Features and Neighboring Images

1
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2
School of Physics and Electronics Technology, Liaoning Normal University, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Received: 14 December 2018 / Revised: 17 January 2019 / Accepted: 21 January 2019 / Published: 30 January 2019
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Abstract

Given a query shoeprint image, shoeprint retrieval aims to retrieve the most similar shoeprints available from a large set of shoeprint images. Most of the existing approaches focus on designing single low-level features to highlight the most similar aspects of shoeprints, but their retrieval precision may vary dramatically with the quality and the content of the images. Therefore, in this paper, we proposed a shoeprint retrieval method to enhance the retrieval precision from two perspectives: (i) integrate the strengths of three kinds of low-level features to yield more satisfactory retrieval results; and (ii) enhance the traditional distance-based similarity by leveraging the information embedded in the neighboring shoeprints. The experiments were conducted on a crime scene shoeprint image dataset, that is, the MUES-SR10KS2S dataset. The proposed method achieved a competitive performance, and the cumulative match score for the proposed method exceeded 92.5% in the top 2% of the dataset, which was composed of 10,096 crime scene shoeprints. View Full-Text
Keywords: shoeprint retrieval; hybrid features; neighboring images; Fourier-Mellin transform; Gabor feature shoeprint retrieval; hybrid features; neighboring images; Fourier-Mellin transform; Gabor feature
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Wu, Y.; Wang, X.; Zhang, T. Crime Scene Shoeprint Retrieval Using Hybrid Features and Neighboring Images. Information 2019, 10, 45.

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