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Descriptor Optimization for Semantic Concept Detection Using Visual Content

Author

Listed:
  • Mohamed Hamroun

    (LabRI- University of Bordeaux, Tunisia, France)

  • Sonia Lajmi

    (MIRACL, University of Sfax, Tunisia, France)

  • Henri Nicolas

    (LabRI- University of Bordeaux, Tunisia, France)

  • Ikram Amous

    (MIRACL, University of Sfax, Tunisia, France)

Abstract

Concept detection has been considered a difficult problem and has attracted the interest of the content-based multimedia retrieval community. This detection implies an association between the concept and the visual content. In other words, the visual characteristics extracted from the video. This includes taking knowledge about the concept itself and its context. This work focuses on the problem of concept detection. For that, several stages are elaborated: first, a method of extraction and semi-automatic annotation of the video plans for the training set is proposed. This new method is based on the genetic algorithm. Then, a preliminary concept detection is carried out to generate the visual dictionary (BoVS). This second step is improved thanks to a noise reduction mechanism. This article's contribution has proven its effectiveness by testing it on a large dataset.

Suggested Citation

  • Mohamed Hamroun & Sonia Lajmi & Henri Nicolas & Ikram Amous, 2019. "Descriptor Optimization for Semantic Concept Detection Using Visual Content," International Journal of Strategic Information Technology and Applications (IJSITA), IGI Global, vol. 10(1), pages 40-59, January.
    • Handle: RePEc:igg:jsita0:v:10:y:2019:i:1:p:40-59
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