Learning an image-word embedding for image auto-annotation on the nonlinear latent space
Wei Liu, Xiaoou Tang · 2005
Latent Semantic Analysis (LSA) has shown encouraging performance for the problem of unsupervised image automatic annotation. LSA conducts annotation by keywords propagation on a linear Latent Space, which accounts for the underlying semantic structure of word and image features. In this paper, we formulate a more general nonlinear model, called Nonlinear Latent Space model, to reveal the latent variables of word and visual features more precisely. Instead of the basic propagation strategy, we present a novel inference strategy for image annotation via Image-Word Embedding (IWE). IWE simultaneously embeds images and words and captures the dependencies between them from a probabilistic viewpoint. Experiments show that IWE-based annotation on the nonlinear latent space outperforms previous unsupervised annotation methods.