Symeon Papadopoulos

Dr. Symeon Papadopoulos is a researcher at the Multimedia Group at the Information Technologies Institute (ITI), part of the Centre for Research and Technology Hellas (CERTH). He is a holder of a PhD and an Engineer degree from Aristotle University of Thessaloniki, an MBA from Blekinge Institute of Technology, and a PDEng degree from Eindhoven University of Technology. He has co-authored more than 40 publications and three patents about multimedia analysis, data mining, social network analysis, information retrieval and multimedia-centric web applications. He actively participates in the EC-funded SocialSensor project, and in the past he had significant contributions to EC-funded projects such as WeKnowIt, GLOCAL, and PATExpert, as well as to industry funded projects. He has led the design and development of the Clusttour web and mobile application. He is a regular reviewer for the journals Multimedia Tools and Applications (MTAP) and Knowledge and Information Systems (KAIS). More information about Symeon can be found on his webpage and on his Twitter and LinkedIn accounts.

Selected topic: One of the current primary research topics of Symeon is the large scale indexing of and knowledge discovery from social multimedia. The massive adoption of media sharing platforms such as Flickr, Instagram, Facebook and YouTube have created huge opportunities for capturing and understanding real-world phenomena, and for powering innovative information retrieval and multimedia-rich applications and services. To this end, it is necessary to devise multimedia indexing and analysis technologies that can cope with the heterogeneity of online content, its huge scale and the prevalence of noisy or low quality content. A promising research direction is the development of compact yet powerful descriptors for near-duplicate search in combination with scalable clustering techniques (e.g. based on community detection on multimedia similarity graphs). In addition, Symeon is interested in multimedia annotation frameworks based on semi-supervised learning that are capable of effectively fusing different features of multimedia content (text-based, visual and social). Integrating the results of the aforementioned approaches with contextual information carried by the multimedia metadata and providing novel information and media exploration interfaces has the potential of disrupting the way we access and search social multimedia content.

Keywords: Social Multimedia, Knowledge Discovery, Multimedia Indexing, Clustering, Multimedia Annotation, Collective Intelligence