Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Annotea: an open RDF infrastructure for shared Web annotations
Proceedings of the 10th international conference on World Wide Web
Advanced Interaction in Context
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Leveraging context to resolve identity in photo albums
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 15th international conference on World Wide Web
CONFOTO: Browsing and annotating conference photos on the Semantic web
Web Semantics: Science, Services and Agents on the World Wide Web
Classification and annotation of digital photos using optical context data
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Spirittagger: a geo-aware tag suggestion tool mined from flickr
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Web Science 2.0: Identifying Trends through Semantic Social Network Analysis
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Analysis of a Real Online Social Network Using Semantic Web Frameworks
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Not all tags are created equal: learning Flickr tag semantics for global annotation
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Query Answering for OWL-DL with rules
Web Semantics: Science, Services and Agents on the World Wide Web
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Manual image annotation is an extensive and a cumbersome task, yet extremely important for image management and retrieval. The purpose of the authors' system is to semi-automatically generate ontology-based annotations for a social network by leveraging the annotations provided by the most active user i.e., the central actor. Context of an image is of central importance in their approach towards semantic semi-automatic annotation. For context of an image, the authors consider several factors like geo-reference, time and relationship among actors in social networks and instead of using image-processing techniques to manipulate and interpret the image, their system leverages the context, which is automatically available along with the image and have also extended Social Network Analysis techniques by considering the granularity of relationships among actors under consideration. The authors use a semantic web framework to represent the social network and to deal with the diversity of relationships. OntoCAIM ontology is developed which not only encompasses Social Network Analysis functionality but also defines mechanism to annotate the images with an underlying ontology.