Descriptor Optimization for Semantic Concept Detection Using Visual Content

Mohamed Hamroun, Sonia Lajmi, Henri Nicolas and Ikram Amous
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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

International Journal of Strategic Information Technology and Applications (IJSITA), 2019, vol. 10, issue 1, 40-59

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsita0:v:10:y:2019:i:1:p:40-59

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