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A Hash Table Approach for Large Scale Perceptual Anchoring
Örebro University, School of Science and Technology.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
2013 (English)In: 2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013), 2013, p. 3060-3066Conference paper, Published paper (Refereed)
Abstract [en]

Perceptual anchoring deals with the problem of creating and maintaining the connection between percepts and symbols that refer to the same physical object. When approaching long term use of an anchoring framework which must cope with large sets of data, it is challenging to both efficiently and accurately anchor objects. An approach to address this problem is through visual perception and computationally efficient binary visual features. In this paper, we present a novel hash table algorithm derived from summarized binary visual features. This algorithm is later contextualized in an anchoring framework. Advantages of the internal structure of proposed hash tables are presented, as well as improvements through the use of hierarchies structured by semantic knowledge. Through evaluation on a larger set of data, we show that our approach is appropriate for efficient bottom-up anchoring, and performance-wise comparable to recently presented search tree algorithm.

Place, publisher, year, edition, pages
2013. p. 3060-3066
Series
IEEE International Conference on Systems Man and Cybernetics Conference Proceedings, ISSN 1062-922X
Keywords [en]
Perceptual anchoring, large scale efficient matching, hash table, binary visual features, semantic categorization
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-34860DOI: 10.1109/SMC.2013.522ISI: 000332201903033ISBN: 978-1-4799-0652-9 (print)OAI: oai:DiVA.org:oru-34860DiVA, id: diva2:714104
Conference
IEEE International Conference on Systems, Man, and Cybernetics (SMC), OCT 13-16, 2013, Manchester, ENGLAND
Available from: 2014-04-25 Created: 2014-04-25 Last updated: 2018-01-11Bibliographically approved

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Persson, AndreasLoutfi, Amy

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf