Towards optimal query design for relevance feedback in image retrieval
We analyze the sub-optimality of traditional greedy active learning based relevance feedback methods in image retrieval, and propose a novel active learning approach to query labels of multiple images together, which minimize the needed round of feedbacks and achieve satisfactory result in a near optimal manner. Our experiments on real image retrieval demonstrate that our solution can yield comparable precession/recall rate by significantly less relevance feedbacks.