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.
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References
TREC Video Track, http://www-nlpir.nist.gov/projects/tv2003/tv2003.html
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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
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DOI: https://doi.org/10.1007/978-3-540-27814-6_11
Publisher Name: Springer, Berlin, Heidelberg
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