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Automatically annotating words for images is a key to semantic-level image retrieval. Recently, several embedding learning based methods achieve good performance in this task which inspires this paper. Here we propose a novel word embedding model in which both images and words can be represented in the same embedding space. The embedding space is learnt in a discriminative nearest neighbor manner such that the annotation information could be propagated among neighbors. In order to acceleratedoi:10.1109/ictai.2012.44 dblp:conf/ictai/ChenYT12 fatcat:qlnvkrmegradnohjalnpoauqsa