The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Multimedia search reranking: A literature survey
Abstract
The explosive growth and widespread accessibility of community-contributed media content on the Internet have led to a surge of research activity in multimedia search. Approaches that apply text search techniques for multimedia search have achieved limited success as they entirely ignore visual content as a ranking signal. Multimedia search reranking, which reorders visual documents based on multimodal cues to improve initial text-only searches, has received increasing attention in recent years. Such a problem is challenging because the initial search results often have a great deal of noise. Discovering knowledge or visual patterns from such a noisy ranked list to guide the reranking process is difficult. Numerous techniques have been developed for visual search re-ranking. The purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. We conclude with several promising directions for future research.
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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
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
A vector space model for automatic indexing
Salton, G., Wong, A., & Yang, C. S. (1975) · Communications of the ACM · doi:10.1145/361219.361220 · Cited by 7,521
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., & Li, H. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273513 · Cited by 1,960
Haveliwala, T. H. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511513 · Cited by 1,567
Cited by
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
Xu, J., Mei, T., Yao, T., & Rui, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.571 · Cited by 1,759
Self-citationA Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance
Liu, X., Liu, W., Mei, T., & Ma, H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_53 · Cited by 641
Self-citationTemporal Relational Ranking for Stock Prediction
Feng, F., He, X., Wang, X., Luo, C., Liu, Y., & Chua, T.-S. (2019) · ACM Transactions on Information Systems · doi:10.1145/3309547 · Cited by 553
Large-scale vehicle re-identification in urban surveillance videos
Liu, X., Liu, W., Ma, H., & Fu, H. (2016) · 2016 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7553002 · Cited by 513
Qian, S., Xue, D., Fang, Q., & Xu, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3188547 · Cited by 68
Metadata
- Authors
- Mei, T., Rui, Y., Li, S., & Tian, Q.
- Venue
- ACM Computing Surveys
- Published
- 2014-01
- Volume
- 46
- Issue
- 3
- Pages
- 1-38
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2013808584
- Cited by
- 192
- References
- 7
- FWCI
- 93.38
- Type
- journal article
- Language
- en
- Open access
- closed
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