Entropy Reduction Strategies on Tree Structured Retrieval Spaces
In this paper, we study the performance of exact retrieval strategies in the case of tree structured retrieval spaces. We assume that the database B is indexed by the leaves of a hierarchical partitioning tree T. We study retrieval processes based on interaction with the user through simple questions attached to the nodes of T as follows: for each node b, the system can display a summary of the subset attached to b (e.g. some typical images) and get the user answer according to the target. We consider retrieval strategies based on step-wise entropy reduction, built on a user model where the answers are independent given the target. We prove an upper bound for the expected number of questions which appears to be nearly optimal in an interesting case. Moreover, we show that at each step, the next question can be found among an adaptive subset of nodes of size log(|ß|). Finally, the overall complexity of the algorithm (for the computer) per retrieval is O(log(∣ß∣)3) whereas the average number of questions (for the user) is O(log(∣ß∣)).
KeywordsMutual Information Image Retrieval User Model Retrieval Strategy Uncertainty Reduction
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