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Learning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (December 2017)  

Public [article]  inThe Visual Computer > vol 33 n° 12 (December 2017) . - pp 1587 - 1600 Titre : Learning aggregated features and optimizing model for semantic labeling Type de document : Article/Communication Auteurs : Jianhua Wang, Auteur ; Chuanxia Zheng, Auteur ; Weihai Chen, Auteur ; Xingming Wu, Auteur Année de publication : 2017 Article en page(s) : pp 1587 - 1600 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attribut [Termes IGN] champ aléatoire conditionnel [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image RVB [Termes IGN] modèle statistique [Termes IGN] scène intérieure [Termes IGN] segmentation d'image Résumé : (Auteur) Semantic labeling for indoor scenes has been extensively developed with the wide availability of affordable RGB-D sensors. However, it is still a challenging task for multi-class recognition, especially for “small” objects. In this paper, a novel semantic labeling model based on aggregated features and contextual information is proposed. Given an RGB-D image, the proposed model first creates a hierarchical segmentation using an adapted gPb/UCM algorithm. Then, a support vector machine is trained to predict initial labels using aggregated features, which fuse small-scale appearance features, mid-scale geometric features, and large-scale scene features. Finally, a joint multi-label Conditional random field model that exploits both spatial and attributive contextual relations is constructed to optimize the initial semantic and attributive predicted results. The experimental results on the public NYU v2 dataset demonstrate the proposed model outperforms the existing state-of-the-art methods on the challenging 40 dominant classes task, and the model also achieves a good performance on a recent SUN RGB-D dataset. Especially, the prediction accuracy of “small” classes has been improved significantly. Numéro de notice : A2017-714 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1302-4 En ligne : https://doi.org/10.1007/s00371-016-1302-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88098 [article]