Abstract
Content-based cross-media retrieval is a new category of retrieval methods by which the modality of query examples and the returned results need not to be the same, for example, users may query images by an example of audio and vice versa. Multimedia Document (MMD) is a set of media objects that are of different modalities but carry the same semantics. In this paper, a graph based approach is proposed to achieve the content-based cross-media retrieval and MMD retrieval. Positive and negative examples of relevance feedback are used differently to boost the retrieval performance and experiments show that the proposed methods are very effective.
This work is supported by National Natural Science Foundation of China (No.60533090, No.60525108), Science and Technology Project of Zhejiang Province (2005C 13032, 2005C11001-05), and China-US Million Book Digital Library Project (www.cadal.zju.edu.cn ).
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Zhuang, Y., Yang, Y. (2006). Boosting Cross-Media Retrieval by Learning with Positive and Negative Examples. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69429-8_17
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DOI: https://doi.org/10.1007/978-3-540-69429-8_17
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