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Learning an image-word embedding for image auto-annotation on the nonlinear latent space

9 years 8 months ago
Learning an image-word embedding for image auto-annotation on the nonlinear latent space
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. Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing—Indexing methods General Terms Algorithms, Theory Keywords...
Wei Liu, Xiaoou Tang
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where MM
Authors Wei Liu, Xiaoou Tang
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