基于关键区域特征提取与二阶段分类网络的场景识别方法OA

In the scene recognition task,there are cases where heterogeneous scenes contain items with high similarity or the spatial layout of similar scenes is too different,that is,the inter-class similarity and intra-class difference of scenes.Existing methods improve the discriminant ability of classifiers by enhancing data sets or using multi-level information complementation.Although some improvements have been made,there are still limitations.In this study,the DPE(Discriminative Patch Extraction)and TSC(Two-Stage Classification)network method are proposed to overcome the inter-class similarity and intra-class difference of scenes.DPE avoids the impact of intra-class differences on scene recognition by preserving the key information regions in images,while the TSC net-work avoids the impact of inter-class similarities on scene recognition by the coarse-fine two-stage training.After combining the proposed method with baseline networks such as ViT(Vision Transformer),the classification accuracy of classical scene recognition data sets Scene15,MITindoor67 and SUN397 reaches 96.9%,88.4%and 76.0%,respectively.The proposed method achieves the highest classification accuracy of 60.5%on the largest scene recog-nition dataset Places365.