Centre de documentation
scientifique

Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs / Abraham Montoya Obeso (2018)  

Public Titre : Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs Type de document : Article/Communication Auteurs : Abraham Montoya Obeso, Auteur ; Jenny Benois-Pineau, Auteur ; Kamel Guissous , Auteur ; Valérie Gouet-Brunet , Auteur ; Mireya S. García Vázquez, Auteur ; Alejandro A. Ramírez Acosta, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IPTA 2018, 8th International Conference on Image Processing Theory, Tools and Applications 07/11/2018 10/11/2018 Xi'an Chine Proceedings IEEE Importance : pp 1 - 6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] Bootstrap (statistique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] exploration de données [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] saillance [Termes IGN] scène urbaine Résumé : (auteur) Incorporating Human Visual System (HVS) models into building of classifiers has become an intensively researched field in visual content mining. In the variety of models of HVS we are interested in so-called visual saliency maps. Contrarily to scan-paths they model instantaneous attention assigning the degree of interestingness/saliency for humans to each pixel in the image plane. In various tasks of visual content understanding, these maps proved to be efficient stressing contribution of the areas of interest in image plane to classifiers models. In previous works saliency layers have been introduced in Deep CNNs, showing that they allow reducing training time getting similar accuracy and loss values in optimal models. In case of large image collections efficient building of saliency maps is based on predictive models of visual attention. They are generally bottom-up and are not adapted to specific visual tasks. Unless they are built for specific content, such as "urban images"-targeted saliency maps we also compare in this paper. In present research we propose a "bootstrap" strategy of building visual saliency maps for particular tasks of visual data mining. A small collection of images relevant to the visual understanding problem is annotated with gaze fixations. Then the propagation to a large training dataset is ensured and compared with the classical GBVS model and a recent method of saliency for urban image content. The classification results within Deep CNN framework are promising compared to the purely automatic visual saliency prediction. Numéro de notice : C2018-097 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IPTA.2018.8608125 Date de publication en ligne : 14/01/2019 En ligne : https://doi.org/10.1109/IPTA.2018.8608125 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95885