Improvement of Multimodal Images Classification Based on DSMT Using Visual Saliency Model Fusion With SVM

  • Hanan Anzid LE2I Laboratory University of Burgundy-Franche
  • Gaetan le Goic LE2I Laboratory University of Burgundy-Franche-Comite France
  • Aissam bekkari IRF-SIC Laboratory Faculty of science Agadir Morocco
  • Alamin Mansouri LE2I Laboratory University of Burgundy-Franche-Comite France
  • Driss Mammass IRF-SIC Laboratory Faculty of science Agadir Morocco
Keywords: Visual saliency model, Data fusion, DSmT formalism, SVM classifier, Dense SURF features, Spectral features, Multimodal images, Classification

Abstract

Multimodal images carry available information that can be complementary, redundant information, and overcomes the various problems attached to the unimodal classification task, by modeling and combining these information together. Although, this classification gives acceptable classification results, it still does not reach the level of the visual perception model that has a great ability to classify easily observed scene thanks to the powerful mechanism of the human brain.

 In order to improve the classification task in multimodal image area, we propose a methodology based on Dezert-Smarandache formalism (DSmT), allowing fusing the combined spectral and dense SURF features extracted from each modality and pre-classified by the SVM classifier. Then we integrate the visual perception model in the fusion process.

To prove the efficiency of the use of salient features in a fusion process with DSmT, the proposed methodology is tested and validated on a large datasets extracted from acquisitions on cultural heritage wall paintings. Each set implements four imaging modalities covering UV, IR, Visible and fluorescence, and the results are promising.

Author Biographies

Hanan Anzid, LE2I Laboratory University of Burgundy-Franche

Laboratory Faculty of science Agadir Morocco and LE2I Laboratory University of Burgundy-Franche-Comite France

Gaetan le Goic, LE2I Laboratory University of Burgundy-Franche-Comite France

LE2I Laboratory University of Burgundy-Franche-Comite France

Aissam bekkari, IRF-SIC Laboratory Faculty of science Agadir Morocco

IRF-SIC Laboratory Faculty of science Agadir Morocco 

Alamin Mansouri, LE2I Laboratory University of Burgundy-Franche-Comite France

LE2I Laboratory University of Burgundy-Franche-Comite France 

Driss Mammass, IRF-SIC Laboratory Faculty of science Agadir Morocco
IRF-SIC Laboratory Faculty of science Agadir Morocco  
Published
2019-01-09
How to Cite
Anzid, H., le Goic, G., bekkari, A., Mansouri, A., & Mammass, D. (2019). Improvement of Multimodal Images Classification Based on DSMT Using Visual Saliency Model Fusion With SVM. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 18, 7418-7430. https://doi.org/10.24297/ijct.v18i0.7956
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Articles