The Retrieval of Shoeprint Images Based on the Integral Histogram of the Gabor Transform Domain

Li, Xiangyang; Wu, Minhua; Shi, Zhiping

doi:10.1007/978-3-662-44980-6_28

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…The results show that the method is robust with respect to partial shoeprints. In [59], Li used the Gabor feature and an integral histogram to retrieve shoeprints. Experiments were conducted on a database containing 2000 shoeprint images, and the accuracy was about 46.8%.…”

Section: Holistic-feature-based Methodsmentioning

confidence: 99%

Crime Scene Shoeprint Image Retrieval: A Review

Dong

Shi

et al. 2022

Electronics

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Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to the query image. In this way, a shoeprint can not only be used as an exhibit for bringing criminal charges but also to provide a clue to a case. The goal of this work is to present an overview of the existing works conducted in shoeprint image retrieval. We detail the different phases of the shoeprint retrieval task and present a summary of the state-of-the-art methods. We analyzed the difficulties and problems in this field and discussed future work directions. This review may help neophytes become involved in research easily and quickly.

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Section: Holistic-feature-based Methodsmentioning

confidence: 99%

Crime Scene Shoeprint Image Retrieval: A Review

Dong

Shi

et al. 2022

Electronics

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“…Xiangyang et al [27] proposed a shoeprint detection system for forensic investigation using Gabor transform and histogram analysis. At first, Gabor transform extracts features and then integral histogram is applied for similarity measure.…”

Section: Footwear Image Enhancement Detection and Matching Methodolomentioning

confidence: 99%

A Survey on Matching of Shoeprint with Reference Footwear in Forensic Study

Mamun¹,

Akter²,

Uddin³

2019

JCC

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Footwear impression marks on the outside surface of shoes are distinctive patterns and an important forensic clue often found at offense scenes. However, in many cases, the footwear mark is treated with improper evidence due to difficulties in visibility and understanding. This paper presents a thorough review of matching algorithms along with enhancement techniques of shoeprint in the forensic study. Finally, it shows some research directions.

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“…using Gabor transform. Li et al [5] combined the integral histogram of the Gabor features with the Euclidean distance and histogram intersection. Kong et al [27] extracted Gabor and Zernike features combined with normalized correlation for matching.…”

Section: A Shoe-print Identificationmentioning

confidence: 99%

“…The similarity metrics in shoe-print identification task including the Euclidean distance [2], [5], [6], [29], [32], the 2D correlation [25], [26], [31], the mean square noise error method (MSNE) [33], and the normalized cross-correlation (NCC) [10], [11]. Other methods in person re-identification and face recognition usually use the cosine distance or the vector angle to measure feature similarity.…”

Section: B Similarity Metricmentioning

confidence: 99%

Shoe-Print Image Retrieval With Multi-Part Weighted CNN

Ding

Wen

et al. 2019

IEEE Access

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Identifying shoe-print impressions in the scene of crime (SoC) from database images is a challenging problem in forensic science due to the complicated impressing surface, the partial absence of on-site impressions, and the huge domain gap between the query and the gallery images. The existing approaches pay much attention to feature extraction while ignoring its distinctive characteristics. In this paper, we propose a novel multi-part weighted convolutional neural network (MP-CNN) for shoe-print image retrieval. Specifically, the proposed CNN model processes images in three steps: 1) dividing the input images vertically into two parts and extracting sub-features by a parameter-shared network individually; 2) calculating the importance weight matrix of the sub-features based on the informative pixels they contained and concatenating them as the final feature, and; 3) using the triplet loss function to measure the similarity between the query and the gallery images. In addition to the proposed network, we adopt an effective strategy to enhance the quality of the images and to reduce the domain gap using the U-Net structure. The experimental evaluations demonstrate that our proposed method significantly outperforms other finegrained cross-domain methods on SPID dataset and obtains comparative results with the state-of-the-art shoe-print retrieval methods on FID300 dataset. INDEX TERMS Cross-domain, image retrieval, shoe-print, scene of crime.

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The Retrieval of Shoeprint Images Based on the Integral Histogram of the Gabor Transform Domain

Cited by 9 publications

References 9 publications

Crime Scene Shoeprint Image Retrieval: A Review

Crime Scene Shoeprint Image Retrieval: A Review

A Survey on Matching of Shoeprint with Reference Footwear in Forensic Study

Shoe-Print Image Retrieval With Multi-Part Weighted CNN

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