1. Cross-Modal Learning to Rank via Latent Joint Representation.
- Author
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Wu, Fei, Jiang, Xinyang, Li, Xi, Tang, Siliang, Lu, Weiming, Zhang, Zhongfei, and Zhuang, Yueting
- Subjects
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MACHINE learning , *RANKING (Statistics) , *IMAGE representation , *IMAGE analysis , *QUERY (Information retrieval system) - Abstract
Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML ^2\textR ). In CML ^2\textR , the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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