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In interactive image search, a user iteratively refines his results by giving feedback on exemplar images. Active selection methods aim to elicit useful feedback, but traditional approaches suffer from expensive selection criteria and cannot predict in formativeness reliably due to the imprecision of relevance feedback. To address these drawbacks, we propose to actively select "pivot" exemplars for which feedback in the form of a visual comparison will most reduce the system's uncertainty. For example, the system might ask, "Is your target image more or less crowded than this image?" Our approach relies on a series of binary search trees in relative attribute space, together with a selection function that predicts the information gain were the user to compare his envisioned target to the next node deeper in a given attribute's tree. It makes interactive search more efficient than existing strategies-both in terms of the system's selection time as well as the user's feedback effort.
Kovashka et al. (Sun,) studied this question.