融合全局与局部特征的鞋印特征提取网络设计

Design of Shoe Print Feature Extraction Network Integrating Global and Local Features

为进一步解决残缺、模糊鞋印的检索困难问题,设计一种使用全局与局部特征联合表达的特征提取网络.一方面,在全局特征分支上对多尺度的鞋印全局特征进行快速归一化加权融合,并对其所有输出分别计算损失;另一方面,在局部特征分支上利用分部特征提取(PCB)模块将鞋印特征图分为3个部分,分别提取3个部分的局部特征并计算其损失.在训练阶段,将全局特征分支与局部特征分支的所有损失相加进行联合表达;在测试阶段,将两分支拼接后的输出直接展平作为待检索鞋印的描述符,并将其与样本库鞋印描述符的余弦距离作为相似性评分.实验结果表明,所提方法大幅降低模型的参数量及计算成本,并在CSS-200、CS-Database和FID-300这3个鞋印数据集上取得较高的准确率,且在CSS-200和CS-Database(Dust)数据集上的top1%取得较好的准确率,分别为94.5%和95.45%.

A feature extraction network using global and local features is designed to tackle the issue of retrieving incomplete and fuzzy shoe prints.The global features of the multiscale shoe print are normalized and weighted,and the losses of all their outputs are calculated;moreover,the partbased Conv baseline(PCB)module is used to divide the shoe print feature map into three parts,extract the local features of the three parts,and calculate their losses.During the training phase,all of the global feature branch and local feature branch losses are added to express them collectively.The output of the two branches after splicing is directly flattened as the shoe print descriptor to be retrieved in the test phase,and the cosine distance between it and the descriptor of the sample library shoe print is used as the similarity score.The experimental results show that the proposed method significantly reduces the parameter quantity and calculation cost of the model,and achieves high accuracy on the three shoe print data sets of CSS-200,CS Database,and FID-300.Furthermore,it achieves decent accuracy on the top1%of the CSS-200 and CS Database(Dust)datasets,which are 94.5%and 95.45%,respectively.