Reasoning and algorithm selection augmented symbolic segmentation

Michitaka Kameyama, Martin Lukac, Kamila Abdiyeva, Alexandra Kim

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


In this paper an alternative method to symbolic segmentation is studied. Semantic segmentation being one of the most difficult tasks currently in the computer vision area, and large number of algorithms is being developed. Thus the proposed approach in this paper exploits this large amount of available computational tools by using the algorithm selection approach. That is, let there be a set A of available algorithms for symbolic segmentation, a set of input features F, a set of image attribute A and a selection mechanism S(F, A, A) that selects on a case by case basis the best algorithm. The semantic segmentation is then an optimization process that combines best component segments from multiple results into a single optimal result. The experiments compare three different algorithm selection mechanisms using three selected semantic segmentation algorithms. The results show that using the current state of art algorithms and relatively low accuracy of algorithm selection the accuracy of the semantic segmentation can be improved by 2%.

Original languageEnglish
Title of host publication2017 Intelligent Systems Conference, IntelliSys 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509064359
Publication statusPublished - Mar 23 2018
Event2017 Intelligent Systems Conference, IntelliSys 2017 - London, United Kingdom
Duration: Sep 7 2017Sep 8 2017


Other2017 Intelligent Systems Conference, IntelliSys 2017
CountryUnited Kingdom


  • Algorithm selection
  • high-level feedback
  • scene understanding
  • semantic segmentation

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Optimization

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