Natural image understanding using algorithm selection and high-level feedback

Martin Lukac, Michitaka Kameyama, Kosuke Hiura

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

6 Citations (Scopus)


Natural Image processing and understanding encompasses hundreds or even thousands of different algorithms. Each algorithm has a certain peak performance for a particular set of input features and configurations of the objects/regions of the input image (environment). To obtain the best possible result of processing, we propose an algorithm selection approach that permits to always use the most appropriate algorithm for the given input image. This is obtained by at first selecting an algorithm based on low level features such as color intensity, histograms, spectral coefficients. The resulting high level image description is then analyzed for logical inconsistencies (contradictions) that are then used to refine the selection of the processing elements. The feedback created from the contradiction information is executed by a Bayesian Network that integrates both the features and a higher level information selection processes. The selection stops when the high level inconsistencies are all resolved or no more different algorithms can be selected.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXX
Subtitle of host publicationAlgorithms and Techniques
Publication statusPublished - 2013
EventIntelligent Robots and Computer Vision XXX: Algorithms and Techniques - Burlingame, CA, United States
Duration: 2013 Feb 42013 Feb 6

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherIntelligent Robots and Computer Vision XXX: Algorithms and Techniques
Country/TerritoryUnited States
CityBurlingame, CA


  • Algorithm Selection
  • Bayesian Networks
  • Contour extraction
  • Image Processing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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