基于改进全局—局部注意网络的室内场景识别方法

由于卷积神经网络(CNN)大多侧重于全局特征学习,忽略了包含更多细节的局部特征信息,使得室内场景识别的准确率难以提高。针对这一问题,提出了基于改进全局—局部注意网络(GLANet)的室内场景识别方法。首先,利用GLANet捕捉场景图像的全局特征和局部特征,增加图像特征中的细节信息;然后,在局部网络中引入non-local注意力模块,通过注意力图和特征图的卷积来进一步保留图像的细节特征,最后融合网络不同阶段的多种特征进行分类。通过在MIT Indoor67和SUN397数据集上的训练和验证,所提方法的识别准确率与LGN方法相比分别提高了1.98%和3.07%。实验结果表明,该算法能够有效捕获全局语义信息和精细的局部细节,显著提高了识别准确率。

Indoor scene recognition method based on improved global-local attention network

Because convolutional neural networks(CNN) mostly focus on global feature learning and ignore local feature information containing more details, it is difficult to improve the accuracy of indoor scene recognition. To solve this problem, this paper proposed an indoor scene recognition method based on improved global-local attention network(GLANet). Firstly, it used GlANet to capture the global and local features of the scene image to increase the detail information of the image features. Then, it introduced a non-local attention module into the local network to further preserve the detail features of the image through the convolution of attention diagram and feature graph. Finally, it fused various features of different stages of the network for classification. Through training and verification on the MIT Indoor67 and the SUN397 dataset, the recognition accuracy of the proposed method is increased by 1.98% and 3.07% respectively compared with the LGN method. Experimental results show that the algorithm can effectively capture global semantic information and fine spatial details, and significantly improves the recognition accuracy.