Selective Multi-cotraining for Video Concept Detection
Pages 443 - 446
Abstract
Research interest in cotraining is increasing which combines information from usually two classifiers to iteratively increase training resources and strengthen the classifiers. We try to select classifiers for cotraining when more than two representations of the data are available. The classifier based on the selected representation or data descriptor is expected to provide the most complementary information as new labels for the target classifier. These labels are critical for the next learning iteration. We present two criteria to select the complementary classifier where classification results on a validation set are used to calculate statistics for all the available classifiers. These statistics are used not only to pick the best classifier but also ascertain the number of new labels to be added for the target classifier. We demonstrate the effectiveness of classifier selection for semantic indexing task on the TRECIVD 2013 dataset and compare it to the self-training.
References
[1]
A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In COLT, 1998.
[2]
W. Du, R. Phlypo, and T. Adali. Adaptive feature split selection for co-training: Application to tire irregular wear classification. In ICASSP, 2013.
[3]
G.-Z. Li, D. Li, W.-C. Lu, J. Y. Yang, and M. Q. Yang. Feature selection for co-training: A qsar study. In IC-AI, 2007.
[4]
X. Li and C. G. M. Snoek. Classifying tag relevance with relevant positive and negative examples. In ACM International Conference on Multimedia, 2013.
[5]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, November 2004.
[6]
K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In CIKM, 2000.
[7]
P. Over, G. Awad, M. Michel, J. Fiscus, G. Sanders, W. Kraaij, A. F. Smeaton, and G. Quéenot. Trecvid 2013 -- an overview of the goals, tasks, data, evaluation mechanisms and metrics. In Proceedings of TRECVID 2013. NIST, USA, 2013.
[8]
S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. ICML, 2007.
[9]
M. D. Smucker, J. Allan, and B. Carterette. A comparison of statistical significance tests for information retrieval evaluation. In ACM-IKM, 2007.
[10]
K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Empowering visual categorization with the gpu. IEEE Transactions on Multimedia, 13(1), 2011.
[11]
A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms, 2008.
[12]
R. Yan and M. R. Naphade. Co-training non-robust classifiers for video semantic concept detection. In ICIP. IEEE, 2005.
[13]
R. Yan and M. R. Naphade. Semi-supervised cross feature learning for semantic concept detection in videos. In CVPR. IEEE, 2005.
Index Terms
- Selective Multi-cotraining for Video Concept Detection
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Published In
April 2014
564 pages
ISBN:9781450327824
DOI:10.1145/2578726
- Conference Chairs:
- Mohan Kankanhalli,
- Stefan Rueger,
- R. Manmatha,
- General Chairs:
- Joemon Jose,
- Keith van Rijsbergen
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Published: 01 April 2014
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ICMR '14 Paper Acceptance Rate 21 of 111 submissions, 19%;
Overall Acceptance Rate 254 of 830 submissions, 31%
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