Hierarchical feature construction for image classification using Genetic Programming

Masanori Suganuma, Daiki Tsuchiya, Shinichi Shirakawa, Tomoharu Nagao

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

9 Citations (Scopus)

Abstract

In this paper, we design a hierarchical feature construction method for image classification. Our method has two feature construction stages: (1) feature construction by a combination of primitive image processing filters, and (2) feature construction by evolved filters. We verify the image classification performance of the proposed method on the MIT urban and nature scene dataset. The experimental results show that the two-stage feature construction improves the classification accuracy compared to single stage feature construction. In addition, the proposed method outperforms several existing feature construction methods.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1423-1428
Number of pages6
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - 2017 Feb 6
Externally publishedYes
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 2016 Oct 92016 Oct 12

Publication series

Name2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Other

Other2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period16/10/916/10/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Optimization
  • Human-Computer Interaction

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