期刊
PATTERN ANALYSIS AND APPLICATIONS
卷 19, 期 4, 页码 1007-1022出版社
SPRINGER
DOI: 10.1007/s10044-015-0478-y
关键词
Cross-modal multimedia retrieval; Topic correlation model; Topic models; Bag-of-features model
资金
- National Science Foundation of China [61305047, 61401012]
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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