Topic correlation model for cross-modal multimedia information retrieval |
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Authors: | Zengchang Qin Jing Yu Yonghui Cong Tao Wan |
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Affiliation: | 1.Intelligent Computing and Machine Learning Lab, School of ASEE,Beihang University,Beijing,China;2.Institute of Information Engineering,Chinese Academy of Sciences,Beijing,China;3.School of Biological Science and Medical Engineering,Beihang University,Beijing,China |
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Abstract: | In this paper, we present a simple and effective topic correlation model (TCM) for cross-modal multimedia retrieval by jointly modeling the text and image components in multimedia documents. In this model, the image component is represented by the bag-of-features model based on local scale-invariant feature transform features, meanwhile the text component is described by a topic distribution learned from a latent topic model. Statistical correlations between these two mid-level features are investigated by mapping them into a semantic space. These cross-modality correlations are used to calculate the conditional probabilities of answers in one modality while given query in the other modality. The model is tested on three cross-modal retrieval benchmark problems including Wikipedia documents in both English and Chinese. Experimental results have demonstrated that the new TCM model achieves the best performance compared to recent state-of-the-art cross-modal retrieval models on the given benchmarks. |
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