Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Social media recommendation based on people and tags
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Towards better TV viewing rates: exploiting crowd's media life logs over Twitter for TV rating
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
A social approach to context-aware retrieval
World Wide Web
DBpedia spotlight: shedding light on the web of documents
Proceedings of the 7th International Conference on Semantic Systems
User behavior in online social networks and its implications: a user study
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
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Online social networks allow users to share their photos with friend, family and the community at large. Social networks are formed with a number of users connected through different relationships, and the strength of these relationships has an influence on the way users react on each other photos. In this paper we investigate how we can assist users to retrieve the most relevant images from their social network. We propose Relation-Based Image Retrieval (RBIR), where social relationships are of central importance. For each user we calculate their relationships with other members in the network, and a ranked list of the closest and most reputed friends is compiled by analyzing the mutual activates between two users and their overall individual reputation in the social network. Comments and likes made by highly ranked members hold more weight, and photos are ranked in accordance with the number and weight of likes and comments they receive. To test our approach, we developed a prototype based on the Facebook platform, allowing users to search for images among their Facebook friends. The results demonstrate that our techniques are useful for retrieving relevant images.