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Learning embeddings for cross-time geographic areas represented as graphs / Margarita Khokhlova (2021)  

Public Titre : Learning embeddings for cross-time geographic areas represented as graphs Type de document : Article/Communication Auteurs : Margarita Khokhlova, Auteur ; Nathalie Abadie , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : SAC 2021, 36th Annual ACM Symposium on Applied Computing 22/03/2021 26/03/2021 en ligne Proceedings ACM Importance : pp 559 - 568 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] arête [Termes IGN] classification par réseau neuronal [Termes IGN] entité géographique [Termes IGN] graphe flou [Termes IGN] image aérienne à axe vertical [Termes IGN] noeud [Termes IGN] relation spatiale [Termes IGN] représentation graphique [Termes IGN] réseau neuronal de graphes Résumé : (auteur) Geographic entities from the vertical aerial images can be viewed as discrete objects and represented as nodes in a graph, linked to each other by edges capturing their spatial relationships. Over time, the natural and man made landscape may evolve and thus also their graph representations. This paper addresses the challenging problem of the retrieval and fuzzy matching of graphs to localize near-identical geographical areas across time. Several use-case scenarios are proposed for the end-to-end learning of a graph embedding using Graph Neural Networks (GNN), along with an effective baseline without learning. The results demonstrate the efficiency of our approach, that enables efficient similarity reasoning for novel hand-engineered cross-time graph data. Code and data processing scripts are available online. Numéro de notice : C2021-002 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3412841.3441936 En ligne : https://doi.org/10.1145/3412841.3441936 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97583

SUMAC'20 : Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2020)  

Public Titre : SUMAC'20 : Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents Type de document : Actes de congrès Auteurs : Valérie Gouet-Brunet , Auteur ; Margarita Khokhlova, Auteur ; Ronak Kosti, Auteur ; Liming Chen, Auteur ; Xu-Chen Yin, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2020 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : SUMAC 2020, 2nd workshop on Structuring and Understanding of Multimedia heritAge Contents 12/10/2020 12/10/2020 en ligne Etats-Unis Proceedings ACM ISBN/ISSN/EAN : 978-1-4503-8155-0 Langues : Anglais (eng) Descripteur : [Termes IGN] conservation du patrimoine [Termes IGN] descripteur [Termes IGN] données massives [Termes IGN] multimedia [Termes IGN] numérisation de photographie [Termes IGN] recherche d'image basée sur le contenu Résumé : (auteur) It is our great pleasure to welcome you to SUMAC 2020, the 2nd edition of the ACM workshop on Structuring and Understanding of Multimedia heritAge Contents. The digitization of large quantities of analogue data and the massive production of born-digital documents for many years now provide us with large volumes of varied multimedia data (images, maps, text, video, multi-sensor data, etc.), an important feature of which is that they are cross-domain. "Cross-domain" reflects the fact that these data may have been acquired in very different conditions: different acquisition systems, times and points of view (e.g. a 1962 postcard from the Arc de Triomphe vs. a recent street-view acquisition by mobile mapping of the same monument). These data represent an extremely rich heritage that can be exploited in a wide variety of fields, from SSH to land use and territorial policies, including smart city, urban planning, tourism, creative media and entertainment. In terms of research in computer science, they address challenging problems related to the diversity and volume of the media across time, the variety of content descriptors (potentially including the time dimension), the veracity of the data, and the different user needs with respect to engaging with this rich material and the extraction of value out of the data. These challenges are reflected in research topics such as multimodal and mixed media search, automatic content analysis, multimedia linking and recommendation, and big data analysis and visualization, where scientific bottlenecks may be exacerbated by the time dimension, which also provides topics of interest such as multimodal time series analysis. Numéro de notice : 17631 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : 10.1145/3423323 En ligne : https://dl.acm.org/doi/proceedings/10.1145/3423323 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97086

Challenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)  

Public Titre : Challenging deep image descriptors for retrieval in heterogeneous iconographic collections Type de document : Article/Communication Auteurs : Dimitri Gominski , Auteur ; Martyna Poreba , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2019 Autre Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Projets : Alegoria / Gouet-Brunet, Valérie Conférence : SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents 21/10/2019 21/10/2019 Nice France Proceedings ACM Importance : pp 31 - 38 Format : 21 x 30 cm Note générale : bibliographie Preprint publié sur ArXiv https://arxiv.org/abs/1909.08866v1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse visuelle [Termes IGN] apprentissage profond [Termes IGN] base de données d'images [Termes IGN] collection [Termes IGN] descripteur [Termes IGN] données hétérogènes [Termes IGN] exploration de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] iconographie [Termes IGN] image multi sources [Termes IGN] indexation [Termes IGN] jeu de données [Termes IGN] recherche d'image basée sur le contenu Résumé : (auteur) This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view contents. For this purpose, we introduce a novel dataset, namely Alegoria dataset, consisting of 12,952 iconographic contents representing landscapes of the French territory, and encapsultating a large range of intra-class variations of appearance which were finely labelled. Six deep features (DELF, NetVLAD, GeM, MAC, RMAC, SPoC) and a hand-crafted local descriptor (ORB) are evaluated against these variations. Their performance are discussed, with the objective of providing the reader with research directions for improving image description techniques dedicated to complex heterogeneous datasets that are now increasingly present in topical applications targeting heritage valorization. Numéro de notice : C2019-022 Affiliation des auteurs : LaSTIG MATIS (2012-2019) Autre URL associée : ArXiv Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3347317.3357246 date de publication en ligne : 19/09/2019 En ligne : https://doi.org/10.1145/3347317.3357246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93623

Voir aussi

• SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2019)  

SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2019)  

Public Titre : SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents Type de document : Article/Communication Auteurs : Valérie Gouet-Brunet , Editeur scientifique ; Margarita Khokhlova, Editeur scientifique ; Liming Chen, Editeur scientifique ; Sander Münster, Editeur scientifique ; Sander Münster Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2019 Projets : 1-Pas de projet / Conférence : MM 2019, 27th ACM International Conference on Multimedia 21/10/2019 25/10/2019 Nice France Proceedings ACM Importance : pp 2726 - 2727 Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information [Termes IGN] bibliothèque numérique [Termes IGN] données massives [Termes IGN] interface homme-machine [Termes IGN] multimedia [Termes IGN] patrimoine culturel [Termes IGN] patrimoine immobilier [Termes IGN] recherche scientifique [Termes IGN] valorisation Résumé : (auteur) SUMAC 2019 is the first workshop on Structuring and Understanding of Multimedia heritAge Contents. It is held in Nice, France on October 21, 2019 and is co-located with the 27th ACM International Conference on Multimedia. Its objective is to present and discuss the latest and most significant trends and challenges in the analysis, structuring and understanding of multimedia contents dedicated to the valorization of heritage, with the emphasis on the unlocking of and access to the big data of the past. A representative scope of Computer Science methodologies dedicated to the processing of multimedia heritage contents and their exploitation is covered by the works presented, with the ambition of advancing and raising awareness about this fully developing research field. Numéro de notice : 25324 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : GEOMATIQUE/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3343031.3350554 En ligne : https://doi.org/10.1145/3343031.3350554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93622

Voir aussi

• Challenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)  

Assessing the planimetric accuracy of Paris atlases from the late 18th and 19th centuries / Bertrand Duménieu (2018)    

Public Titre : Assessing the planimetric accuracy of Paris atlases from the late 18th and 19th centuries Type de document : Article/Communication Auteurs : Bertrand Duménieu , Auteur ; Nathalie Abadie , Auteur ; Julien Perret , Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2018 Projets : 1-Pas de projet / Conférence : KEGeod 2018, Knowledge Extraction from Geographical Data, Technical tracks of SAC 2018 09/04/2018 13/04/2018 Pau France , SAC 2018, 33rd Annual ACM Symposium on Applied Computing 09/04/2018 13/04/2018 Pau France Proceedings ACM Importance : pp 876 - 883 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte ancienne [Termes IGN] dix-huitième siècle [Termes IGN] dix-neuvième siècle [Termes IGN] géoréférencement [Termes IGN] géoréférencement indirect [Termes IGN] Paris (75) [Termes IGN] point d'appui [Termes IGN] précision planimétrique [Termes IGN] projection Résumé : (auteur) The recent initiatives to digitize cultural heritage resources and publish them on the Web have renewed interest in historical maps for the diachronic analysis of territories in GIS applications. However, such analyses should not be done without a good understanding of the possibilities and limitations of geographical information provided by historical maps, i.e. their quality. One of the major concerns regarding historical maps quality is their positional planimetric accuracy which highly depends on survey techniques used at the time. As these techniques are not always thoroughly known and as ground truth is most of the time not sufficiently available, direct absolute evaluation approaches have been proposed to assess historical maps positional planimetric accuracy. In this article, we follow the intuition that the most widely adopted georeferencing-based approach for assessing the positional planimetric accuracy of historical maps can be adapted to provide an evaluation of the error caused by the survey process in cases like Paris atlases where the georeferencing transformation can be estimated with ground control points based on geodetic features and where the projection of the map can be approximated by a well known projected coordinate reference system. We apply this tuned approach on the Verniquet atlas and evaluate the validity of our hypothesis about projection approximation. Numéro de notice : C2018-019 Affiliation des auteurs : LaSTIG COGIT+Ext (2012-2019) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3167132.3167228 date de publication en ligne : 15/04/2018 En ligne : https://doi.org/10.1145/3167132.3167228 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90481

Documents numériques

en open access Assessing the planimetric accuracy of Paris atlases ... - postprint Adobe Acrobat PDF

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Proceedings of the 9th Workshop on Geographic Information Retrieval, GIR'2015 / Ross S. Purves (2015)  

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TEXTOMAP : determining geographical window for texts / Geoffrey Brun (2015)  

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2D arrangement-based hierarchical spatial partitioning: an application to pedestrian network generation / Murat Yirci (2013)  

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