The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A new approach to cross-modal multimedia retrieval
Abstract
The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling correlations between the two components, and 2) this modeling is more effective in feature spaces with higher levels of abstraction. Correlations between the two components are learned with canonical correlation analysis. Abstraction is achieved by representing text and images at a more general, semantic level. The two hypotheses are studied in the context of the task of cross-modal document retrieval. This includes retrieving the text that most closely matches a query image, or retrieving the images that most closely match a query text. It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy. The cross-modal model is also shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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Cited by
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,314
Deep Multimodal Representation Learning: A Survey
Guo, W., Wang, J., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2916887 · Cited by 535
An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges
Peng, Y., Huang, X., & Zhao, Y. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2705068 · Cited by 341
CCL: Cross-modal Correlation Learning With Multigrained Fusion by Hierarchical Network
Peng, Y., Qi, J., Huang, X., & Yuan, Y. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2742704 · Cited by 228
Metadata
- Authors
- Rasiwasia, N., Costa Pereira, J., Coviello, E., Doyle, G., Lanckriet, G. R. G., Levy, R., & Vasconcelos, N.
- Venue
- Proceedings of the 18th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2010-10-25
- Pages
- 251-260
- OpenAlex
- W2106277773
- Cited by
- 1,334
- References
- 3
- FWCI
- 21.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rasiwasia, N., Costa Pereira, J., Coviello, E., Doyle, G., Lanckriet, G. R. G., Levy, R., & Vasconcelos, N. (2010). A new approach to cross-modal multimedia retrieval. Proceedings of the 18th ACM international conference on Multimedia. https://doi.org/10.1145/1873951.1873987
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