Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval

Zhuang, Yue-Ting, Yang, Yi and Wu, Fei (2008) Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. IEEE Transactions on Multimedia, 10 2: 221-229. doi:10.1109/TMM.2007.911822

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Author Zhuang, Yue-Ting
Yang, Yi
Wu, Fei
Title Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval
Journal name IEEE Transactions on Multimedia   Check publisher's open access policy
ISSN 1520-9210
Publication date 2008-02
Sub-type Article (original research)
DOI 10.1109/TMM.2007.911822
Volume 10
Issue 2
Start page 221
End page 229
Total pages 9
Place of publication IEEE
Publisher Piscataway, NJ, United States
Language eng
Abstract Although multimedia objects such as images, audios and texts are of different modalities, there are a great amount of semantic correlations among them. In this paper, we propose a method of transductive learning to mine the semantic correlations among media objects of different modalities so that to achieve the cross-media retrieval. Cross-media retrieval is a new kind of searching technology by which the query examples and the returned results can be of different modalities, e.g., to query images by an example of audio. First, according to the media objects features and their co-existence information, we construct a uniform cross-media correlation graph, in which media objects of different modalities are represented uniformly. To perform the cross-media retrieval, a positive score is assigned to the query example; the score spreads along the graph and media objects of target modality or MMDs with the highest scores are returned. To boost the retrieval performance, we also propose different approaches of long-term and short-term relevance feedback to mine the information contained in the positive and negative examples.
Keyword Cross-media retrieval
Multimedia document
Multimedia semantics mining
Relevance feedback
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: ERA 2012 Admin Only
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 49 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 06 Mar 2012, 13:28:15 EST by Mr Mathew Carter on behalf of School of Information Technol and Elec Engineering