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Latent semantic factorization for multimedia representation learning
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DOI：10.1007/s11042-017-5135-6.png)
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Abstract
En 中文
Due to the rapid development of multimedia applications, cross-media semantics learning is becoming increasingly important nowadays. One of the most challenging issues for cross-media semantics understanding is how to mine semantic correlation between different modalities. Most traditional multimedia semantics analysis approaches are based on unimodal data cases and neglect the semantic consistency between different modalities. In this paper, we propose a novel multimedia representation learning framework via latent semantic factorization (LSF). First, the posterior probability under the learned classifiers is served as the latent semantic representation for different modalities. Moreover, we explore the semantic representation for a multimedia document, which consists of image and text, by latent semantic factorization. Besides, two projection matrices are learned to project images and text into a same semantic space which is more similar with the multimedia document. Experiments conducted on three real-world datasets for cross-media retrieval, demonstrate the effectiveness of our proposed approach, compared with state-of-the-art methods.
Keywords:
Posterior probability
Latent semantic factorization
Cross-modal retrieval
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