Context Centric Approach of Semantic Image Annotation and Retrieval
To assist users to annotate images in social network, I use existing metadata gathered from already annotated images on social networks, to generate metadata for non-annotated images. Social network analysis techniques together with image metadata are used to automatically annotate images. As context for an image, I consider temporal and geographical values. In addition to that, I consider three basic social entities associated with images; user relationships, user activities (comments and likes) and annotations.
To retrieve the most relevant images from social network, I proposed Relation-Based Image Retrieval (RBIR). For each user I 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, I developed a prototype based on the Facebook platform, to annotate images and allow users to search for images among their Facebook friends. The results demonstrate that our techniques are useful for annotation and retrieving relevant images.
Has part(s)Paper I: Elahi, N. & Karlsen, R. (2012). User behavior in online social networks and its implications: a user study. WIMS`12: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, 61. Also available at https://doi.org/10.1145/2254129.2254204.
Paper II: Elahi, N., Karlsen, R. & Younas, W. (2012). Ontology-Based Image Annotation by Leveraging Social Context. International Journal of Handheld Computing Research (IJHCR), 3(3), 53-66. Also available at https://doi.org/10.4018/jhcr.2012070104.
Paper III: Elahi, N. & Karlsen, R. (2014). Relation based image retrieval in online social network. ICUIMC`14: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, 26. Also available at http://doi.acm.org/10.1145/2557977.2558019.
Paper IV: Elahi, N., Karlsen, R. & Holsbo, E.J. (2013). Personalized Photo Recommendation By Leveraging User Modeling On Social Network. IIWAS`13: Proceedings of International Conference on Information Integration and Web-based Applications & Services, 68-71. Also available at https://doi.org/10.1145/2539150.2539232.
Paper V: Karlsen, R., Evertsen, M.H. & Elahi, N. (2013). Metadatabased automatic image tagging. International Journal of Metadata, Semantics and Ontologies, 8(4), 298-308. Also available at https://doi.org/10.1504/IJMSO.2013.058412.
Paper VI: Mannan, N.B., Sarwar, S.M. & Elahi, N. (2014). A New User Similarity Computation Method for Collaborative Filtering Using Artificial Neural Network. In: Mladenov, V., Jayne, C. & Iliadis, L. (Eds.), Engineering Applications of Neural Networks, 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings, 145-154. Springer Nature. Also available at https://doi.org/10.1007/978-3-319-11071-4.
Paper VIII: Elahi, N., Karlsen, R. & Akselsen, A. (2009). A context centric approach for semantic image annotation and retrieval. 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns. Available in the file “thesis_entire.pdf”. Also available at 10.1109/ComputationWorld.2009.30.
Paper IX: Karlsen, R., Elahi, N. & Andersen, A. (2018). Personalized Recommendation of Socially Relevant Images. WIMS`18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, 41. Also available at https://doi.org/10.1145/3227609.3227672.
PublisherUiT Norges arktiske universitet
UiT The Arctic University of Norway
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