Activity learning from lifelogging images

Kader Belli, Emre Akbaş, Adnan Yazici

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The analytics of lifelogging has generated great interest for data scientists because big and multi-dimensional data are generated as a result of lifelogging activities. In this paper, the NTCIR Lifelog dataset is used to learn activities from an image point of view. Minute definitions are classified into activity classes using images and annotations, which serve as a basis for various classification techniques, namely SVMs and convolutional neural network structures (CNN), for learning activities. The performance of the classification methods used in this study is evaluated and compared.

Original languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing - 18th International Conference, ICAISC 2019, Proceedings
EditorsRyszard Tadeusiewicz, Witold Pedrycz, Jacek M. Zurada, Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski
PublisherSpringer Verlag
Pages327-337
Number of pages11
ISBN (Print)9783030209148
DOIs
Publication statusPublished - Jan 1 2019
Event18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019 - Zakopane, Poland
Duration: Jun 16 2019Jun 20 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11509 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019
CountryPoland
CityZakopane
Period6/16/196/20/19

Keywords

  • CNN
  • Deep learning
  • Image classification
  • Lifelogging
  • Machine learning
  • ResNet-50
  • SVM
  • Text classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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