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Object recognition using saliency maps and HTM learning
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
2012 (English)In: Imaging Systems and Techniques (IST), 2012 IEEE International Conference on, IEEE , 2012, 528-532 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems.

Place, publisher, year, edition, pages
IEEE , 2012. 528-532 p.
Keyword [en]
HTM network, object recognition, saliency map
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-114313DOI: 10.1109/IST.2012.6295575Scopus ID: 2-s2.0-84870706712ISBN: 978-145771774-1 (print)OAI: oai:DiVA.org:kth-114313DiVA: diva2:588368
Conference
2012 IEEE International Conference on Imaging Systems and Techniques, IST 2012, 16 July 2012 through 17 July 2012, Manchester
Note

QC 20130115

Available from: 2013-01-15 Created: 2013-01-15 Last updated: 2013-09-05Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • 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