Name
Abstract | ||
|---|---|---|
A shoeprint is a valuable clue found at a crime scene and plays a significant role in forensic investigations. In this paper, in order to maintain the local features of a shoeprint image and place a pattern in a block, a novel automatic method was proposed, referred to as Modified Multi-Block Local Binary Pattern (MMB-LBP). In this method, shoeprint images are divided into blocks according to two different models. The histograms of all blocks of the first and second models are separately measured and stored in the first and second feature matrices, respectively. The performance evaluations of the proposed method were carried out by comparing with state-of-the-art methods. The evaluation criteria are the successful retrieval rates obtained using the best match score at rank one and cumulative match score for the first five matches. The comparison results indicated that the proposed method performs better than other methods, in terms of retrieval of complete and incomplete shoeprints. That is, the proposed method was able to retrieve 97.63% of complete shoeprints, 96.5% of incomplete toe shoeprints, and 91.18% of incomplete heel shoeprints. Moreover, the experiments showed that the proposed method is significantly resistant to the rotation, salt and pepper noise, and Gaussian white noise distortions in comparison with the other methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/sym13020296 | SYMMETRY-BASEL |
Keywords | DocType | Volume |
automatic image retrieval, feature extraction, local binary pattern (LBP), MMB-LBP, shoeprint, similarity measurement | Journal | 13 |
Issue | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sayyad Alizadeh | 1 | 0 | 0.34 |
| Hossein Barghi Jond | 2 | 0 | 0.34 |
| Vasif V. Nabiyev | 3 | 0 | 0.34 |
| Cemal Köse | 4 | 0 | 0.34 |