Computer Science ›› 2019, Vol. 46 ›› Issue (12): 306-312.doi: 10.11896/jsjkx.191200500C

• Interdiscipline & Frontier • Previous Articles     Next Articles

Semi-supervised Scene Recognition Method Based on Multi-mode Fusion

SHEN Hong1,2,3, LIU Jun-fa1,2,3, CHEN Yi-qiang1,2,3, JIANG Xin-long1,2,3, HUANG Zheng-yu2,3   

  1. (University of Chinese Academy of Sciences,Beijing 100190,China)1;
    (Research Center for Ubiquitous Computing Systems,Institute of Computing Technology,Chinese Academyof Sciences,Beijing 100190,China)2;
    (Beijing Key Laboratory of Mobile Computing and Pervasive Device,Chinese Academy of Sciences,Beijing 100190,China)3
  • Received:2019-01-11 Online:2019-12-15 Published:2019-12-17

Abstract: Scene recognition is an important part of pervasive computing.It aims to provide users with accurate persona-lized service and improve service quality by identifying the location of the smartphone users.In the actual environment,there are two problems in accurate scene recognition.Firstly,based on single mode sensor data or wireless signal data,classification effect is not good enough ,and its generalization is not enough.Secondly,scene recognition accuracy depends on a large number of labeled data,resulting in high cost.In view of these problems,a semi-supervised scene recognition method based on multi-mode fusion was proposed.The menthod makes full use of the complementary information of Wi-Fi,Bluetooth and sensors to improve the accuracy of recognition.Compared with the recognition based on single mode data,fusion feature can increase the static scene classification accuracy by 10%.In this paper,a semi-supervised learning method was constructed to solve the problem of high data acquisition cost in dynamic scene,and the classification accuracy is over 90% by reducing half of the labeled data.The results show that introducing semi-supervised lear-ning method based on the complementary advantages of Wi-Fi,Bluetooth and sensors information can reduce data collecting cost and improve scene recognition accuracy to some extent,and thus highly increase its recognition accuracy and universality.

Key words: Scene recognition, Wi-Fi, Bluetooth, Multi-mode fusion, Semi-supervised learning

CLC Number: 

  • TP311
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