Crime Scene Shoeprint Retrieval Using Hybrid Features and Neighboring Images

Yanjun Wu,Tao Zhang,Xinnian Wang

doi:10.3390/info10020045

Yanjun Wu, Tao Zhang + Show 1 more

Open Access

https://doi.org/10.3390/info10020045

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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.

Highlights

Shoeprint retrieval aims at retrieving the most similar shoeprints that were collected at different crime scenes, to help investigators to reveal clues about a particular case
(2) We propose a neighborhood-based similarity estimation (NSE) method, which utilizes the information contained in neighbors to improve the performance of a shoeprint retrieval method
The greatest difference, compared to the other existing shoeprint retrieval methods, is that it considers the relationship between every two shoeprints, and the relationship between their neighbors; (3) We propose a generic manifold based reranking framework, which can narrow the well-known gap between high-level semantic concepts and low-level features; (4) The proposed method can work well for real crime scene shoeprint image retrieval

Summary

Introduction

Shoeprint retrieval aims at retrieving the most similar shoeprints that were collected at different crime scenes, to help investigators to reveal clues about a particular case. Large numbers of crime scene shoeprint images were collected and recorded for analysis. When there was a new case, investigators could manually compare shoeprints derived at the crime scene with those collected from other crime scenes to reveal clues. It is really difficult and tedious to conduct this work for a huge number of degraded shoeprints. It is necessary to propose a more efficient automatic shoeprint retrieval method. In the past few years, many shoeprint image retrieval methods have been proposed, and they have demonstrated a good performance in forensic investigations. From the perspective of methodology, most of these shoeprint retrieval methods fall into two categories:

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