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

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

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Field Value
 
Title Improvement of Multimodal Images Classification Based on DSMT Using Visual Saliency Model Fusion With SVM
 
Creator Anzid, Hanan
le Goic, Gaetan
bekkari, Aissam
Mansouri, Alamin
Mammass, Driss
 
Subject Visual saliency model
Data fusion
DSmT formalism
SVM classifier
Dense SURF features
Spectral features
Multimodal images
Classification
 
Description 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.
 
Publisher KHALSA PUBLICATIONS
 
Date 2019-01-09
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://cirworld.com/index.php/ijct/article/view/7956
10.24297/ijct.v18i0.7956
 
Source INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY; Vol 18; 7418-7430
2277-3061
 
Language eng
 
Relation http://cirworld.com/index.php/ijct/article/view/7956/7634
 
Rights Copyright (c) 2019 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
https://creativecommons.org/licenses/by/4.0
 

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