Semi-supervised cross feature learning for semantic concept detection in videos

@article{Yan2005SemisupervisedCF,
  title={Semi-supervised cross feature learning for semantic concept detection in videos},
  author={Rong Yan and Milind R. Naphade},
  journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  year={2005},
  volume={1},
  pages={657-663 vol. 1}
}
For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. But a major obstacle to this is the insufficiency of labeled training samples. Multi-view semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data. However, one of their assumptions requiring that each view be sufficient for learning is usually violated in semantic concept detection. In this paper, we propose… CONTINUE READING
Highly Cited
This paper has 89 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 61 extracted citations

89 Citations

051015'07'10'13'16'19
Citations per Year
Semantic Scholar estimates that this publication has 89 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 16 references

IBM research TRECVID-2003 video retrieval system

A. Amir, M. Berg, S. F. Chang
NIST TRECVID-2003, Nov 2003. • 2003
View 4 Excerpts
Highly Influenced

http://www-nlpir.nist.gov/projects/trecvid

TRECVID TREC Video Retrieval Evaluation
.
View 4 Excerpts
Highly Influenced

Matching Words and Pictures

Journal of Machine Learning Research • 2003
View 1 Excerpt

Similar Papers

Loading similar papers…