Abstract
Video retrieval compares multimedia queries to a video collection in multiple dimensions and combines all the retrieval scores into a final ranking. Although text are the most reliable feature for video retrieval, features from other modalities can provide complementary information. This paper presents a reranking framework for video retrieval to augment retrieval based on text features with other evidence. We also propose a boosted reranking algorithm called Co-Retrieval, which combines a boosting type algorithm and a noisy label prediction scheme to automatically select the most useful weak hypotheses for different queries. The proposed approach is evaluated with queries and video from the 65-hour test collection of the 2003 NIST TRECVID evaluation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
TREC Video Track, http://www-nlpir.nist.gov/projects/tv2003/tv2003.html
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. In: Proc. of ICML 1998, pp. 170–178 (1998)
Collins, M., Schapire, R.E., Singer, Y.: Logistic regression, adaboost and breg-man distances. In: COLT, pp. 158–169 (2000)
Collins, M.: Discriminative reranking for natural language parsing. In: Proc. 17th Intl. Conf. on Machine Learning, pp. 175–182 (2000)
Tieu, K., Viola, P.: Boosting image retrieval. In: Intl. Conf. on Computer Vision, pp. 228–235 (2001)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: COLT (1998)
Hauptmann, A.G., et al.: Informedia at trecvid 2003: Analyzing and searching broadcast news video. In: Proc. of (VIDEO) TREC 2003, Gaithersburg, MD (2003)
Yan, R., Hauptmann, A.G.: The combination limit of multimedia retrieval. In: Proc. of ACM Multimedia 2003 (2003)
Ratsch, G., Onoda, T., Muller, K.-R.: Soft margins for AdaBoost. Machine Learning 42(3), 287–320 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, R., Hauptmann, A.G. (2004). Co-retrieval: A Boosted Reranking Approach for Video Retrieval. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds) Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27814-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-27814-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22539-3
Online ISBN: 978-3-540-27814-6
eBook Packages: Springer Book Archive