To search for images in a small collection, it can be done by just looking at them one-by-one. The sizes of image collections on the web or professional collections are in the order of a hundred thousand if not a million. For such collections systems should provide efficient browsing techniques. As most of the time, users are non-expert searchers the systems must have a user friendly interface. To satisfy these requirements, we design image search systems that allow the user to interact with image collections in an intuitive way. To that end, advanced visualization techniques are used in which a cloud of images is presented on the screen in such a way that similar images are presented close to each other. In this way the user's attention is pointed to the right search direction. While exploring this direction the user can give feedback to the system by indicating relevant images. The system than learns to adapt itself to get closer to the user's search expectation. We have demonstrated our proposed approach to different image collections from simple to very complicated ones such as images taken from large news video archives. The experimental results show a significant improvement in search performance over existing methods.

This thesis makes a contribution to the field of multimedia understanding. Where our ultimate aim is to structure the digital multimedia chaos by bridging the semantic gap between computable data features on one end and the semantic interpretation of the data by a user on the other end. We distinguish between produced and non-produced multimedia or video documents. We depart from the view that a produced video is the result of an authoring-driven production process. This authoring process serves as a metaphor for machine-driven understanding. We present a step-by-step extrapolation of this authoring metaphor for automatic multimedia understanding. While doing so, we cover in this thesis an extensive overview of the field, a theoretical foundation for authoring-driven multimedia understanding, state-of-the-art benchmark validation, and practical semantic video retrieval applications.