scenario optimization for interactive category search
Most of the existing work in interactive content based retrieval concentrates on machine learning methods for effective use of relevance feedback. On the other end of the spectrum, the information visualization community focusses on effective methods for conveying information to the user. What lacks is research considering the information visualization and interactive content based retrieval as truly integrated parts of one search system. In such an integrated system there are many degrees of freedom like the number of images to display, the image size, different visualization modes, and possible feedback modes. To find optimal values for all of those using user studies is unfeasible. We therefore develop scenarios in which tasks and user actions are simulated. These are then optimized based on objective constraints and evaluation criteria. In such a manner the degrees of freedom are reduced and the remaining degrees can be evaluated in user studies. In this paper we present a system which integrates advanced similarity based visualization with active learning. We have performed extensive scenario based experimentation on an interactive category search task. The results show that indeed the use of advanced visualization and active learning pays off.
relevance feedback similarity based visualization active learning interactive search