Geo-based automatic image annotation

Authors:
Hatem Mousselly Sergieh;Gabriele Gianini;Mario Döller;Harald Kosch;Elöd Egyed-Zsigmond;Jean-Marie Pinon
Affiliations:
INSA de Lyon, Villeurbanne, France;University of Milan, Italy;University of Passau, Passau, Germany;University of Passau, Passau, Germany;INSA de Lyon, Villeurbanne, France;INSA de Lyon, Villeurbanne, France
Venue:
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Year:
2012

Citing 13
Cited 2

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
The Google Similarity Distance

IEEE Transactions on Knowledge and Data Engineering
Dual cross-media relevance model for image annotation

Proceedings of the 15th international conference on Multimedia
How flickr helps us make sense of the world: context and content in community-contributed media collections

Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age

ACM Computing Surveys (CSUR)
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding
Learning to tag

Proceedings of the 18th international conference on World wide web
NUS-WIDE: a real-world web image database from National University of Singapore

Proceedings of the ACM International Conference on Image and Video Retrieval
Learning social tag relevance by neighbor voting

IEEE Transactions on Multimedia
Feature map hashing: sub-linear indexing of appearance and global geometry

Proceedings of the international conference on Multimedia
PCA-SIFT: a more distinctive representation for local image descriptors

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated annotation of landmark images using community contributed datasets and web resources

SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies

Retrieving geo-location of videos with a divide & conquer hierarchical multimodal approach

Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging

Proceedings of the 5th ACM Multimedia Systems Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

A huge number of user-tagged images are daily uploaded to the web. Recently, a growing number of those images are also geotagged. These provide new opportunities for solutions to automatically tag images so that efficient image management and retrieval can be achieved. In this paper an automatic image annotation approach is proposed. It is based on a statistical model that combines two different kinds of information: high level information represented by user tags of images captured in the same location as a new unlabeled image (input image); and low level information represented by the visual similarity between the input image and the collection of geographically similar images. To maximize the number of images that are visually similar to the input image, an iterative visual matching approach is proposed and evaluated. The results show that a significant recall improvement can be achieved with an increasing number of iterations. The quality of the recommended tags has also been evaluated and an overall good performance has been observed.