Abstract
To exploit the hidden group structures of data and thus detect concepts in videos, this paper proposes a novel group sparse ensemble learning approach based on Automatic Group Sparse Coding (AutoGSC). We first adopt AutoGSC to learn both a common dictionary over different data groups and an individual group-specific dictionary for each data group which can help us to capture the discrimination information contained in different data groups. Next, we represent each data instance by using a sparse linear combination of both dictionaries. Finally, we propose an algorithm to use the reconstruction errors of data instances to calculate the ensemble gating function for ensemble construction and fusion. Experiments on the TRECVid 2008 benchmark show that the ensemble learning proposal achieves promising results and outperforms existing approaches.
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.
References
Amir, A., Berg, M., Chang, S.-F., et al.: Ibm research trecvid-2003 video retrieval system. In: NIST TRECVID Workshop (November 2003)
Cao, J., Lan, Y., Li, J., et al.: Intelligent multimedia group of Tsinghua University at trecvid 2006. In: NIST TRECVID Workshop (November 2006)
Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)
Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2179–2195 (2009)
Huiskes, M.J., Thomee, B., Lew, M.S.: New trends and ideas in visual concept detection: the mir flickr retrieval evaluation initiative. In: MIR 2010, New York, NY, USA, pp. 527–536 (2010)
Jiang, Y.-G., Yang, J., Ngo, C.-W., Hauptmann, A.G.: Representations of keypoint-based semantic concept detection: A comprehensive study. IEEE Transactions on Multimedia 12, 42–53 (2010)
Li, H., Wang, X., Tang, J., Zhao, C.: Combining global and local matching of multiple features for precise retrieval of item images. ACM/Springer Multimedia System Journal 19(1), 37–49 (2013)
Munder, S., Gavrila, D.: An experimental study on pedestrian classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1863–1868 (2006)
Over, P., Awad, G., Rose, R.T., Fiscus, J.G., Kraaij, W., Smeaton, A.F.: Trecvid 2008 - goals, tasks, data, evaluation mechanisms and metrics. In: NIST TRECVID Workshop (2008)
Pytlik, B., Ghoshal, A., Karakos, D., Khudanpur, S.: TRECVID 2005 Experiment at Johns Hopkins University: Using Hidden Markov Models for Video Retrieval. In: NIST TRECVID Workshop (November 2005)
Samy Bengio, D.S., Pereira, F., Singer, Y.: Group Sparse Coding. In: Neural Information Processing Systems - NIPS (2009)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Song, Y., Zheng, Y.-T., Tang, S., et al.: Localized multiple kernel learning for realistic human action recognition in videos. IEEE Transactions on Circuits and Systems for Video Technology 21(9) (2011)
Tang, S., Li, J.-T., Li, M., Xie, C., Liu, Y., Tao, K., Xu, S.-X.: Trecvid 2008 high-level feature extraction by MCG-ICT-CAS. In: NIST TRECVID Workshop (November 2008)
Tang, S., Li, J.-T., Yong-Dong Zhang, E.: Pornprobe: an lda-svm based pornography detection system. In: ACM Multimedia 2009 (October 2009)
Tang, S., Zheng, Y.-T., Cao, G., Zhang, Y.-D., Li, J.-T.: Ensemble learning with lda topic models for visual concept detection. In: Multimedia - A Multidisciplinary Approach to Complex Issues, pp. 175–200 (2012)
Tang, S., Zheng, Y.-T., Wang, Y., Chua, T.-S.: Sparse ensemble learning for concept detection. IEEE Transactions on Multimedia 14(1), 43–54 (2012)
Wang, F., Lee, N., Sun, J., Hu, J.: Automatic Group Sparse Coding. In: Twenty-Fifth AAAI Conference on Artificial Intelligence (August 2011)
Zhu, S., Wang, G., Ngo, C.-W., Jiang, Y.-G.: On the sampling of web images for learning visual concept classifiers. In: CIVR 2010, New York, NY, USA, pp. 50–57 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Sun, Y., Sudo, K., Taniguchi, Y. (2013). Group Sparse Ensemble Learning for Visual Concept Detection. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-03731-8_60
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
Online ISBN: 978-3-319-03731-8
eBook Packages: Computer ScienceComputer Science (R0)