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Un géotag est un marqueur (« tag », en anglais) à caractère géographique inséré dans des fichiers audio ou raster dans champs (JPEG, , ), IPTC (JPEG/), XMP (très nombreux formats). Le terme en français est « balise de géolocalisation ». Cette balise peut notamment contenir :

des données GPS ou GNSS (coordonnées latitude et longitude, une altitude)
la direction de vue (boussole)
des informations saisies par l'utilisateur : lieux de saisie, code postal…

D'autres données non cartographiques peuvent être associées comme :

des données de temps : heure de capture
des données spécifiques aux périphériques de capture : appareil photo (en particulier, la focale), appareil audio (échantillonnage, codage en bit)

Trois manières principales sont aujourd'hui utilisées pour insérer cette information dans les fichiers : le geotagging à la volée avec un appareil branché sur l'appareil photo, voire avec l'appareil photo lui-même (si le modèle de celui-ci intègre une telle fonctionnalité

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Image Localization using Google Street View

Raffaele Lauro

Digital images, taken for example with a smartphone, are usually geo-tagged with location and viewing direction information. This information is not always accurate due to, for example, GPS inaccuracies in large cities. The aim of this project is to develop an algorithm that takes an image and estimates the location and viewing direction by key-point matching with Google street-view data. As a first step, the original image will be geo-tagged allowing an easy pruning of the street view data. The project could then progress to localizing a non-geo-tagged image using some additional location information, such as a street name or point of interest. This type of algorithm could have applications localizing the large number of images on the Internet with unreliable or no geo-tagging information. In addition, one can envision such a system being employed in indoor environments where GPS currently fails. The student will be expected to deliver software to take an image with imprecise geo-tagging and match it to Google street images with a similar location. The algorithm should return the location and orientation of the best matching street-view image. There are two possible extensions to this main requirement: 1. Use matches in multiple street-view images to estimate a more accurate location and orientation, between the street-view images. 2. Improve the recognition stage so that the algorithm can deal with less accurate geo-tagging.

2015

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Comparative Study of Trust Modeling For Automatic Landmark Tagging

Touradj Ebrahimi, Ivan Ivanov, Péter Vajda

Many images uploaded to social networks are related to travel, since people consider traveling to be an important event in their life. However, a signiﬁcant amount of travel images on the Internet lack proper geographical annotations or tags. In many cases, the images are tagged manually. One way to make this time-consuming manual tagging process more efﬁcient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In this paper, we present a system for automatic geotag propagation in images based on the similarity between image content (famous landmarks) and its context (associated geotags). In such scenario, however, a wrong or a spam tag can damage the integrity and reliability of the automated propagation system. Therefore, for reliable geotags propagation, we suggest adopting user trust model based on a social feedback from the users of the photosharing system. We compare this socially-driven approach with other user trust models via experiments and subjective testing on an image database of various famous landmarks. Results demonstrate that relying on a user feedback is more efﬁcient, since number of propagated tags more than doubles without the loss in accuracy compared to using other models or propagating without trust modeling.

Ieee-Inst Electrical Electronics Engineers Inc2013

Chargement

Geotag Propagation with User Trust Modeling

Touradj Ebrahimi, Ivan Ivanov, Jong Seok Lee, Péter Vajda

The amount of information that people share on social networks is constantly increasing. People also comment, annotate, and tag their own content (videos, photos, notes, etc.), as well as the content of others. In many cases, the content is tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In such a scenario, however, a wrong or a spam tag can damage the integrity and reliability of the automated propagation system. Users may make mistakes in tagging, or irrelevant tags and content may be added maliciously for advertisement or self-promotion. Therefore, a certain mechanism insuring the trustworthiness of users or published content is needed. In this chapter, we discuss several image retrieval methods based on tags, various approaches to trust modeling and spam protection in social networks, and trust modeling in geotagging systems. We then consider a specific example of automated geotag propagation system that adopts a user trust model. The tag propagation in images relies on the similarity between image content (famous landmarks) and its context (associated geotags). For each tagged image, similar untagged images are found by the robust graph-based object duplicate detection and the known tags are propagated accordingly. The user trust value is estimated based on a social feedback from the users of the photo-sharing system and only tags from trusted users are propagated. This approach demonstrates that a practical tagging system significantly benefits from the intelligent combination of efficient propagation algorithm and a user-centered trust model.

Springer-Verlag2013

Chargement

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