Large Scale Image Annotation via Deep Representation Learning and Tag Embedding Learning

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Abstract
In this paper, we focus on the issue of large scale image annotation, whereas most existing methods are devised for small datasets. A novel model based on deep representation learning and tag embedding learning is proposed. Specifically, the proposed model learns an unified latent space for image visual features and tag embeddings simultaneously. Furthermore, a metric matrix is introduced to estimate the relevance scores between images and tags. Finally, an objective function modeling triplet relationships (irrelevant tag, image, relevant tag) is proposed with maximum margin pursuit. The proposed model is easy to tackle new images and tags via online learning and has a relatively low test computation complexity. Experimental results on NUS-WIDE dataset demonstrate the effectiveness of the proposed model.
Funding Information
  • National Natural Science Foundation of China (61331018)
  • Beijing Natural Science Foundation (4142057)
  • National Basic Research Program of China (2012CB316304)

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