The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones
Abstract
Mobile phones are becoming increasingly sophisticated with a rich set of on-board sensors and ubiquitous wireless connectivity. However, the ability to fully exploit the sensing capabilities on mobile phones is stymied by limitations in multimedia processing techniques. For example, search using cellphone images often encounters high error rate due to low image quality.
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Citations
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
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
Automated Flower Classification over a Large Number of Classes
Nilsback, M.-E., & Zisserman, A. (2008) · 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing · doi:10.1109/icvgip.2008.47 · Cited by 3,442
MAUI: making smartphones last longer with code offload
Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., & Bahl, P. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814441 · Cited by 2,281
Cited by
A survey of mobile phone sensing
Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292
Crowdsourced Data Management: A Survey
Li, G., Wang, J., Zheng, Y., & Franklin, M. J. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2535242 · Cited by 283
A location-based incentive mechanism for participatory sensing systems with budget constraints
Jaimes, L. G., Vergara-Laurens, I., & Labrador, M. A. (2012) · 2012 IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2012.6199855 · Cited by 256
Learning from crowdsourced labeled data: a survey
Zhang, J., Wu, X., & Sheng, V. S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9491-9 · Cited by 203
Metadata
- Authors
- Yan, T., Kumar, V., & Ganesan, D.
- Venue
- Proceedings of the 8th international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2010-06-15
- Pages
- 77-90
- OpenAlex
- W2138965424
- Cited by
- 255
- References
- 4
- FWCI
- 26.44
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yan, T., Kumar, V., & Ganesan, D. (2010). CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones. Proceedings of the 8th international conference on Mobile systems, applications, and services. https://doi.org/10.1145/1814433.1814443
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
