Comparison of visual datasets for machine learning

Kent Gauen, Ryan Dailey, John Laiman, Yuxiang Zi, Nirmal Asokan, Yung Hsiang Lu, George K. Thiruvathukal, Mei Ling Shyu, Shu Ching Chen

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

10 Scopus citations

Abstract

One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new approach creating datasets using real-time, geo-tagged visual data, greatly improving the contextual information of the data. The data could be automatically labeled by cross-referencing information from other sources (such as weather).

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
EditorsLatifur Khan, Balaji Palanisamy, Chengcui Zhang, Sahra Sedigh Sarvestani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages346-355
Number of pages10
ISBN (Electronic)9781538615621
DOIs
StatePublished - Nov 8 2017
Event18th IEEE International Conference on Information Reuse and Integration, IRI 2017 - San Diego, United States
Duration: Aug 4 2017Aug 6 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
Volume2017-January

Other

Other18th IEEE International Conference on Information Reuse and Integration, IRI 2017
CountryUnited States
CitySan Diego
Period8/4/178/6/17

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems

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