Using the Wavelet Transform to Learn from User Feedback
Ilaria Bartolini, Paolo Ciaccia, Florian M. Waas · 2000
User feedback has proven very successful to query large multimedia databases. Due to the nature of the data representation and the mismatch between mathematical models and human perception, the query techniques benefit substantially from interactively modifying a query. Typical examples are generalized ellipsoid queries where optimal ratios and orientations of the half-axes are determined by relevance feedback. However, no information about the outcome of a feedback process is stored whatsoever once the process is terminated. Accordingly, the entire feedback loop has to be repeated---starting out with default parameters---if the same query is posed again. In this paper we present preliminary results on how to preserve feedback results in a space efficient way and learn from user feedback. The cornerstone of our system are multidimensional unbalanced wavelets that are used to store the parameters determined during the feedback process. Using wavelets lets us not only store parameter combinations but also enables us to predict parameter settings for queries similar to earlier ones by interpolation: the feedback process for an entirely new query can be started with a parameter setting, usually much closer to the optimal than the default parameters. As a result, after an initial learning phase, feedback is needed for fine tuning only, increasing effectiveness and response time of multimedia databases. 1