Abstract: Material SEM images are rich in content,and traditional retrieval methods and general-domain retrieval methods are easily affected byvarious factors such as image distortion and complex textures in image feature extraction,resulting in suboptimal extraction of key features.Aiming at the shortcomimgs of conventional methods in feature extraction and efficient retrieval of material SEM images,this paper proposes an image retrieval method based on multi-scale feature information,integratingAtrous Spatial Pyramid Pooling(ASPP) and an enhanced convolutional block attention module(ECBAM).This method employs the ConvNeXt network for feature extraction,leveraging the advantages of dilated convolutions with large receptive fields and residual networks to capture more details and complex textures,effectively extracting both local and global features.Additionally,by incorporating the latest Mamba module and modifying it into a bidirectional architecture to integrate CBAM,the enhanced mixed attention mechanism ECBAM is proposed.The combination of ASPP and ECBAM ensures stable and efficient feature fusion and enhancement.Experimental results demonstrate that this method achieves superior retrieval performance on material SEM image datasets,with an average retrieval accuracy improvement of 1.5% compared to mainstream retrieval methods.

Key words: Micro image, Image retrieval, ASPP, Hybrid attention mechanism, Mamba