A Mobile Vision Service for Multimedia Tourist Applications in Urban Environments

Paletta, Lucas; Fritz, Gerald; Seifert, Christin; Luley, P.M.; Almer, Alexander

doi:10.1109/itsc.2006.1706801

Cited by 6 publications

(3 citation statements)

References 16 publications

(16 reference statements)

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“…Such systems have been proposed before (e.g. [18,20]), but the automatic collection of the database from user-generated content has not been addressed yet.…”

Section: Linking To Wikipediamentioning

confidence: 99%

“…The resulting "cleaned" image database for the mined objects and events is of far higher quality than the original data and facilitates a variety of applications. For example, the mined structure can be used for automated annotation of photos uploaded to community collections, for retrieval and browsing of landmark buildings [19], automatic 3D reconstruction of sights [31,9], or for mobile phone tourist guide applications [18], where users can point the integrated camera to a sight and retrieve information about it.…”

Section: Introductionmentioning

confidence: 99%

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World-scale mining of objects and events from community photo collections

Quack

Leibe

Gool

2008

Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval

236

240

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In this paper, we describe an approach for mining images of objects (such as touristic sights) from community photo collections in an unsupervised fashion. Our approach relies on retrieving geotagged photos from those web-sites using a grid of geospatial tiles. The downloaded photos are clustered into potentially interesting entities through a processing pipeline of several modalities, including visual, textual and spatial proximity. The resulting clusters are analyzed and are automatically classified into objects and events. Using mining techniques, we then find text labels for these clusters, which are used to again assign each cluster to a corresponding Wikipedia article in a fully unsupervised manner. A final verification step uses the contents (including images) from the selected Wikipedia article to verify the cluster-article assignment. We demonstrate this approach on several urban areas, densely covering an area of over 700 square kilometers and mining over 200,000 photos, making it probably the largest experiment of its kind to date.

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“…Such systems have been proposed before (e.g. [18,20]), but the automatic collection of the database from user-generated content has not been addressed yet.…”

Section: Linking To Wikipediamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

World-scale mining of objects and events from community photo collections

Quack

Leibe

Gool

2008

Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval

236

240

View full text Add to dashboard Cite

show abstract

“…Paletta et al (Paletta et al, 2006) defined a specific system devoted to the improvement in the description of the images' salient points, called "informative-SIFT".…”

Section: Related Workmentioning

confidence: 99%

GAIL: Geometry-aware Automatic Image Localization

Benedetti

Corsini

Dellepiane

et al. 2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:The access and integration of the massive amount of information, that can be provided by the web, can be of great help in a number of fields, including tourism and advertising of artistic sites. A "virtual visit" of a place can be a valuable experience before, during and after the experience on-site. For this reason, the contribution from the public could be merged to provide a realistic and immersive visit of known places. We propose an automatic image localization system, which is able to recognize the site that has been framed, and calibrate it on a pre-existing 3D representation. The system is characterized by very high accuracy and it is able to validate, in a completely unsupervised manner, the result of the localization. Given an unlocalized image, the system selects a relevant set of pre-localized images, performs a Structure from Motion partial reconstruction of this set and then obtain an accurate camera calibration of the image with respect to the model by minimizing distances between projections on the model surface of corresponding image features. The accuracy reached is enough to seamlessly view the input image correctly super-imposed in the 3D scene.

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