Computer Science ›› 2022, Vol. 49 ›› Issue (4): 209-214.doi: 10.11896/jsjkx.210100135

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Scene Recognition Method Based on Multi-level Feature Fusion and Attention Module

XU Hua-jie1,2, QIN Yuan-zhuo1, YANG Yang1   

  1. 1 College of Computer and Electronic Information, Guangxi University, Nanning 530004, China;
    2 Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, China
  • Received:2021-01-18 Revised:2021-05-20 Published:2022-04-01
  • About author:XU Hua-jie,born in 1974,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include artificial intelligence,acoustic signal recognition and computer vision.YANG Yang,born in 1995,postgra-duate.Her main research interests include artificial intelligence and compu-ter vision.
  • Supported by:
    This work was supported by the Science and Technology Plan Project of Guangxi Zhuang Autonomous Region(2017AB15008) and Science and Technology Plan Project of Chongzuo(FB2018001).

Abstract: Scene image is usually composed of background information and foreground objects.Convolutional neural network (CNN) used for scene recognition task usually needs to recognize the category of scene according to the characteristics of key objects in the scene, or even combined with the position relationship between objects.Aiming at the problem that the key target features of small size in the scene image gradually disappear with the deepening of the network level, which leads to scene recognition errors, a scene recognition method based on multi-level feature fusion and attention module is proposed.Firstly, the feature extraction part of the deep neural network ResNet-18 is divided into five branches, and then the multi-level features of the output of the five branches are fused, and the fused features are used for scene recognition and classification to make up for the lost target information.Secondly, an improved attention module is added to the network to achieve the purpose of focusing on learning the key targets in the scene image, so as to improve the recognition effect further.Experimental results on several scene datasets show that the recognition accuracy of the proposed method on MIT-67, SUN-397 and UIUC-Sports scene datasets reaches 88.2%, 79.9% and 97.7% respectively, which is higher than the current mainstream scene recognition methods.

Key words: Attention module, Convolutional neural network, Feature fusion, Scene recognition

CLC Number: 

  • TP391
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