Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Context data in geo-referenced digital photo collections
Proceedings of the 12th annual ACM international conference on Multimedia
ITEMS: intelligent travel experience management system
Proceedings of the international workshop on Workshop on multimedia information retrieval
Sunrise: towards location based clustering for assisted photo management
Proceedings of the 2007 workshop on Tagging, mining and retrieval of human related activity information
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Large Scale Tag Recommendation Using Different Image Representations
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
MMEDIA '10 Proceedings of the 2010 Second International Conferences on Advances in Multimedia
Learning landmarks by exploiting social media
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Social interactions over geographic-aware multimedia systems
Proceedings of the 21st ACM international conference on Multimedia
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Nowadays, an enormous number of photographic images are uploaded on the Internet by casual users. In this study, we consider the concept of embedding geographical identification of locations as geotags in images. We attempt to retrieve images having certain similarities (or identical objects) from a geotagged image dataset. We then define the images having identical objects as orthologous images. Using content-based image retrieval (CBIR), we propose a ranking function--orthologous identity function (OIF)--to estimate the degree to which two images contain similarities in the form of identical objects; OIF is a similarity rating function that uses the geographic distance and image distance of photographs. Further, we evaluate the OIF as a ranking function by calculating the mean reciprocal rank (MRR) using our experimental dataset. The results reveal that the OIF can improve the efficiency of retrieving orthologous images as compared to using only geographic distance or image distance.