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Connecting images through time and sources: Introducing low-data, heterogeneous instance retrieval / Dimitri Gominski (2021)
Titre : Connecting images through time and sources: Introducing low-data, heterogeneous instance retrieval Type de document : Article/Communication Auteurs : Dimitri Gominski Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] base de données d'images
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] données d'apprentissage
[Termes descripteurs IGN] données hétérogènes
[Termes descripteurs IGN] exploration de données
[Termes descripteurs IGN] extraction de traits caractéristiques
[Termes descripteurs IGN] image multi sources
[Termes descripteurs IGN] indexation sémantique
[Termes descripteurs IGN] précision de la classification
[Termes descripteurs IGN] recherche d'image basée sur le contenuRésumé : (auteur) With impressive results in applications relying on feature learning, deep learning has also blurred the line between algorithm and data. Pick a training dataset, pick a backbone network for feature extraction, and voilà; this usually works fora variety of use cases. But the underlying hypothesis that there exists a training dataset matching the use case is not alwaysmet. Moreover, the demand for interconnections regardless of the variations of the content calls for increasing generalization and robustness in features. An interesting application characterized by these problematics is the connection of historical and cultural databases of images.Through the seemingly simple task of instance retrieval, wepropose to show that it is not trivial to pick features respondingwell to a panel of variations and semantic content. Introducing anew enhanced version of the ALEGORIA benchmark, we compare descriptors using the detailed annotations. We further give in sights about the core problems in instance retrieval, testing fourstate-of-the-art additional techniques to increase performance. Numéro de notice : P2021-004 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : sans date de publication en ligne : 21/03/2021 En ligne : https://arxiv.org/pdf/2103.10729.pdf Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97398
Titre : Improving image description with auxiliary modality for visual localization in challenging conditions Type de document : Article/Communication Auteurs : Nathan Piasco Année de publication : 2021 Projets : PLaTINUM / Gouet-Brunet, Valérie Article en page(s) : pp 185 - 202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] localisation basée image
[Termes descripteurs IGN] localisation basée visionRésumé : (auteur) Image indexing for lifelong localization is a key component for a large panel of applications, including robot navigation, autonomous driving or cultural heritage valorization. The principal difficulty in long-term localization arises from the dynamic changes that affect outdoor environments. In this work, we propose a new approach for outdoor large scale image-based localization that can deal with challenging scenarios like cross-season, cross-weather and day/night localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We show through extensive evaluation that our method can improve localization performances, especially in challenging scenarios when the visual appearance of the scene has changed. Our method is able to leverage both visual and geometric clues from monocular images to create discriminative descriptors for cross-season localization and effective matching of images acquired at different time periods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Finally we extended our method to reflectance modality and we compare multi-modal descriptors respectively based on geometry, material reflectance and a combination of both. Numéro de notice : A2021-132 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-020-01363-6 date de publication en ligne : 28/08/2020 En ligne : https://doi.org/10.1007/s11263-020-01363-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96971
in International journal of computer vision > vol 29 n° 1 (January 2021) . - pp 185 - 202[article]
Titre : Semantic relatedness algorithm for keyword sets of geographic metadata Type de document : Article/Communication Auteurs : Zugang Chen, Auteur ; Yaping Yang, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 140 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] Infrastructure de données
[Termes descripteurs IGN] internet interactif
[Termes descripteurs IGN] métadonnées géographiques
[Termes descripteurs IGN] relation sémantique
[Termes descripteurs IGN] similitude sémantique
[Termes descripteurs IGN] système à base de connaissances
[Termes descripteurs IGN] terminologie
[Termes descripteurs IGN] thesaurusRésumé : (auteur) Advances in linked geospatial data, recommender systems, and geographic information retrieval have led to urgent necessity to assess the overall semantic relatedness between keyword sets of geographic metadata. In this study, a new model is proposed for computing the semantic relatedness between arbitrary two keyword sets of geographic metadata stored in current global spatial data infrastructures. In this model, the overall semantic relatedness is derived by pairing these keywords that are found to be most relevant to each other and averaging their relatedness. To find the most relevant keywords across two keyword sets precisely, the keywords in the keyword set of geographic metadata are divided into three kinds: the thesaurus elements, the WordNet elements, and the statistical elements. The thesaurus-lexical relatedness measure (TLRM), the extended thesaurus-lexical relatedness measure (ETLRM), and the Longest Common Substring method are proposed to compute the semantic relatedness between two thesaurus elements, two WordNet elements, a thesaurus element, and a WordNet element and two statistical elements, respectively. A human data set – the geographic-metadata’s keyword set relatedness dataset, which was used to evaluate the precision of the semantic relatedness measures of keyword sets of geographic metadata, was created. The proposed method was evaluated against the human-generated relatedness judgments and was compared with the Jaccard method and Vector Space Model. The results demonstrated that the proposed method achieved a high correlation with human judgments and outperformed the existing methods. Finally, the proposed method was applied to quantitatively linked geospatial data. Numéro de notice : A2020-057 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1647797 date de publication en ligne : 20/09/2017 En ligne : https://doi.org/10.1080/15230406.2019.1647797 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94573
in Cartography and Geographic Information Science > vol 47 n° 2 (February 2020) . - pp 125 - 140[article]
Titre : Cross-year multi-modal image retrieval using siamese networks Type de document : Article/Communication Auteurs : Margarita Khokhlova, Auteur ; Valérie Gouet-Brunet Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : ICIP 2020, 27th IEEE International Conference on Image Processing 25/10/2020 28/10/2020 Abou Dhabi Emirats Arabes Unis Proceedings IEEE Importance : pp 2361 - 2365 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] recherche d'image basée sur le contenu
[Termes descripteurs IGN] réseau neuronal siamois
[Termes descripteurs IGN] segmentation sémantiqueRésumé : (auteur) This paper introduces a multi-modal network that learns to retrieve by content vertical aerial images of French urban and rural territories taken about 15 years apart. This means it should be invariant against a big range of changes as the (nat-ural) landscape evolves over time. It leverages the original images and semantically segmented and labeled regions. The core of the method is a Siamese network that learns to extract features from corresponding image pairs across time. These descriptors are discriminative enough, such that a simple kNN classifier on top, suffices as final geo-matching criteria. The method outperformed SOTA "off-the-shelf" image descrip-tors GEM and ResNet50 on the new aerial images dataset. Numéro de notice : C2020-015 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP40778.2020.9190662 date de publication en ligne : 01/10/2020 En ligne : https://doi.org/10.1109/ICIP40778.2020.9190662 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95684 SUMAC'20 : Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2020)
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 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 descripteurs IGN] conservation du patrimoine
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] données massives
[Termes descripteurs IGN] multimedia
[Termes descripteurs IGN] numérisation de photographie
[Termes descripteurs IGN] recherche d'image basée sur le contenuRésumé : (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
