Towards a pervasive cloud computing based food image recognition

  • Wenshan Wang
  • , Pengcheng Duan
  • , Weishan Zhang
  • , Faming Gong
  • , Peiying Zhang
  • , Yuan Rao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Food image recognition is challenging due to the diversity of food, and color, light, view angles' effect on food image. The recognition process is also a computation heavy process. Therefore, We also propose to use pervasive cloud computing paradigm to improve the performance of food image recognition. Based on empirical and experimental explorations, we propose to use SIFT(Scale Invariant Feature Transform) and Gabor descriptors as food image features and KMeans algorithm for feature clustering. Evaluations show that the proposed approach can give acceptable recognition rate with good performance gains.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Pages2243-2244
Number of pages2
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 - Beijing, China
Duration: 20 Aug 201323 Aug 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

Conference

Conference2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Country/TerritoryChina
CityBeijing
Period20/08/1323/08/13

Keywords

  • Gabor
  • Image recognition
  • KMeans
  • Pervasive Cloud Computing
  • SIFT

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