Design and evaluation of human-machine communication for image information mining

Very large volumes of heterogenous data, like multimedia, Earth observation images, scientific and engineering measurements, for instance, are continuously generated and stored. A typical case is the field of Earth observation. The widespread availability of high resolution images does not only explore the volumes of data, but also brings order at magnitude in the image detail, thus enormously increasing the information content. However, today's concepts and technologies are still limited in communicating the information content to people for use in real life applications. In this paper, we overview a new concept for knowledge-driven image information mining (KIM) and both analyze and evaluate it from the perspective of human-machine communication. The KIM concept enables the information communication from a very large image repository to users via the Internet. The communication is at a semantic level of representation and is adapted to the user's conjecture.