The pervasive use of Online Social Networks (OSN) for networking, communication and search in tandem with the ubiquitous availability of smartphones, which enables real-time multimedia capturing and sharing, have led to massive amounts of user-generated content and activities being amassed online, and made publicly available for analysis and mining. Leveraging large amounts of user-generated content creates new exciting possibilities for a number of applications and services such as search and forecasting.

Digital images and videos are nowadays present in everyone’s life, and we are all both consumers and producers of digital audio-visual material. For us humans, it’s pretty easy to assess such visual information: just by viewing an image or a video, we can easily identify the objects and actions or events depicted in it; we get an impression of the quality of the digital medium (e.g. if a photo is blurred, it’s just obvious to us) and of the aesthetics of it (i.

Nowadays, the proliferation of content due to the internet penetration and the easy creation of user-generated content have raised the need for the research and development of efficient search and retrieval techniques. Depending on the application, there is a need for the research and development of information retrieval techniques that deal with browsing and search of heterogeneous multimedia, web search and crawling, multimodal fusion of information, as well as knowledge extraction.

Virtual and augmented reality are currently considered among the technologies with the highest expected impact on user experience. Next to the low-cost and high-performance hardware for VR and AR, we may add the 3D digitization of objects and scenes that is becoming mainstream, as well as the ability to interlink content. All the above constitute the perfect setting for allowing the remote and on-site visitors of museums and cultural sites, to enjoy a drastically different experience.

The Semantic Web technologies provide the means and tools to unanimously represent the meaning of Web entities and their relations to one another, in a machine-interpretable manner. While modelling and managing knowledge for web entities has long ago been established, the Semantic Web technologies also show great potential in other domains, in both online and offline worlds. Two such prominent applications have to do with representing and managing context, enabling two different flavours of context-aware computing.

The use of electroencephalograms (EEG) for conducting Brain Research has been one of the most exciting research fields gaining more and more attention. The potential to tap into the human’s brain and understand its secrets carries the promise of improving our lives in many different ways. With the aim to fulfill this promise, MKLab has setup a Lab with highly sophisticated equipment (i.e. ranging from a high-density EEG system (EGI 300 Geodesic EEG system – GES300) and the SMI Red500 eye tracker with high refresh rate, all the way to the lightweight EEG devices of Emotiv Epoc+ and Emotiv Insight) in order to perform research along two related fronts:

Collective Awareness Platforms for Sustainability and Social Innovation (CAPS) are ICT systems leveraging the emerging “network effect” by combining open data, information gleaned from social media, distributed knowledge creation and data from real environments (“Internet of Things”) in order to create awareness of problems and possible solutions requesting collective efforts, enabling new forms of social innovation. CAPS is part of the research priorities of the Horizon 2020 research programme of the European Commission and was also part of the last FP7 research programme.

Large-scale multimedia indexing and search Development of scalable methods for performing content-based image search in massive image collections. Research involves a) the extension of state-of-the-art feature extraction, aggregation and indexing schemes for speeding up near-duplicate search, while maintaining competitive levels of retrieval accuracy, and b) the application and extension of dimensionality reduction and approximate computation techniques to the problem of concept detection, in both massive scale and incremental settings. Multimedia analysis using knowledge and context We study semantic multimedia analysis not merely as a problem of machine learning but more from the perspective of how to optimally utilize explicit knowledge and contextual information to boost the analysis efficiency.

The widespread proliferation of digital media, and in particular video data, in recent years, has made the automated analysis of its content necessary for retrieval, storage, transmission, security and commercial purposes. Video content is analysed for the detection and recognition of human activities in a variety of setups, ranging from constrained indoors environments to outdoors locations and videos “in the wild” (such as YouTube© content). Applications include health monitoring for ambient assisted living, analysis of online content, effective classification.