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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...
Graph-based ranking models, such as manifold ranking (MR), have been widely used in various image retrieval applications. To further improve such models, a current trend is to fuse the ranking results from multiple feature sets. Most of existing methods mainly concentrate on fusing the homogeneous feature sets derived from a single information channel, like the multiple modalities of image visual...
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