Clickthrough refinement for improved graph ranking

Graph ranking is a promising technique for image retrieval, but its effectiveness is limited by the so-called semantic gap. To mitigate this gap, clickthroughs, which are helpful to perceive the visual content of images, are adopted by graph ranking models recently. However, few existing models take both sparseness and noisiness of clickthroughs into account, which are important in refining the clickthrough-based search results. In this paper, we propose a novel solution to clickthrough refinement that consists of two key components. The first one is to prune the noisy clickthroughs by a neighbor voting strategy, and the second one is to enrich the incomplete clickthroughs by a Tri-CF (collaborative filtering) algorithm. Furthermore, we devise four solutions to affinity matrices fusion in order to leverage the clickthroughs and visual features within a unified graph ranking model. Our extensive experiments for clickthrough prediction and image retrieval validate the effectiveness of the proposed techniques in comparison to state-of-the-art approaches.