Incorporating semantic knowledge for visual lifelog activity recognition

Min Huan Fu, An Zi Yen, Hen Hsen Huang, Hsin Hsi Chen

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

4 Scopus citations

Abstract

The advance in wearable technology has made lifelogging more feasible and more popular. Visual lifelogs collected by wearable cameras capture every single detail of individual's life experience, offering a promising data source for deeper lifestyle analysis and better memory recall assistance. However, building a system for organizing and accessing visual lifelogs is a challenging task due to the semantic gap between visual data and semantic descriptions of life events. In this paper, we introduce semantic knowledge to reduce such a semantic gap for daily activity recognition and lifestyle understanding. We incorporate the semantic knowledge derived from external resources to enrich the training data for the proposed supervised learning model. Experimental results show that incorporating external semantic knowledge is beneficial for improving the performance of recognizing life events.

Original languageEnglish
Title of host publicationICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages450-456
Number of pages7
ISBN (Electronic)9781450370875
DOIs
StatePublished - 8 Jun 2020
Event10th ACM International Conference on Multimedia Retrieval, ICMR 2020 - Dublin, Ireland
Duration: 8 Jun 202011 Jun 2020

Publication series

NameICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval

Conference

Conference10th ACM International Conference on Multimedia Retrieval, ICMR 2020
Country/TerritoryIreland
CityDublin
Period8/06/2011/06/20

Keywords

  • Lifelog
  • Lifelog activity recognition
  • NTCIR lifelog dataset
  • Semantic knowledge
  • Word embedding

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