基于多尺度自注意卷积的足迹压力图像检索算法

为了提高足迹压力图像检索的精度,提出基于多尺度自注意卷积的足迹压力图像检索算法.首先,对足迹压力图像进行角度校正、对齐、擦除等预处理操作,减小图像角度等因素对特征提取的影响.再由多个并行分支的空洞卷积和自适应注意模块构成的多尺度自注意卷积模块自适应地提取可判别特征.最后,由全局特征分支、残缺性评分掩模分支构成残缺性评分模块,得到共同残缺性评分矩阵,利用该评分矩阵对可判别特征进行加权组合,提高网络对残缺足迹共同可见区域的关注程度.实验表明,在构建的FootPrintImage数据集上,文中算法具有较高的首中准确率和平均检索精度.

Footprint Pressure Image Retrieval Algorithm Based on Multi-scale Self-attention Convolution

To improve the accuracy of footprint pressure image retrieval,a footprint pressure image retrieval algorithm based on multi-scale self-attention convolution is proposed.Firstly,preprocessing operations,such as angle correction,alignment and erasure,are carried out to reduce the influence of image angle on feature extraction.Secondly,the discriminative features are extracted adaptively by the multi-scale self-attention convolution module composed of the hole convolution with multiple parallel branches and the self-attention module.Finally,the common incomplete scoring matrix is obtained by the incomplete scoring module composed of global feature branches and incomplete score mask branches.The discriminative features are weighted and combined via the common incomplete scoring matrix to improve the attention of the network to the common visible area of incomplete footprints.The experimental results show that the proposed algorithm produces higher first hit accuracy and average retrieval accuracy on the constructed FootPrintImage dataset compared with some existing image retrieval methods.