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2015 | OriginalPaper | Buchkapitel

A Multiple Image Group Adaptation Approach for Event Recognition in Consumer Videos

verfasst von : Dengfeng Zhang, Wei Liang, Hao Song, Zhen Dong, Xinxiao Wu

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

Event recognition in the consumer videos is a challenging task since it is difficult to collect a large number of labeled training videos. In this paper, we propose a novel Multiple kernel Image Group Adaptation approach to divide the training labeled Web images into several semantic groups and optimize the combinations of each based kernel. Our method simultaneously learns a kernel function and a robust Support Vector Regression (SVR) classifier by minimizing both the structure risk of SVR with the smooth assumption and the distribution difference of weighted image groups and the consumer videos. Comprehensive experiments on the datasets CCV and TREATED 2014 demonstrate the effectiveness of our method for event recognition.

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Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.P., Schölkopf, B., Smola, A.J.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14), e49–e57 (2006)CrossRef

Bruzzone, L., Marconcini, M.: Domain adaptation problems: a DASVM classification technique and a circular validation strategy. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 770–787 (2010)CrossRef

Chattopadhyay, R., Sun, Q., Fan, W., Davidson, I., Panchanathan, S., Ye, J.: Multisource domain adaptation and its application to early detection of fatigue. ACM Trans. Knowl. Discov. Data (TKDD) 6(4), 18 (2012)

Duan, L., Tsang, I.W., Xu, D.: Domain transfer multiple kernel learning. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 465–479 (2012)CrossRef

Duan, L., Xu, D., Chang, S.F.: Exploiting web images for event recognition in consumer videos: a multiple source domain adaptation approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1338–1345. IEEE (2012)

Duan, L., Xu, D., Tsang, I.W.: Domain adaptation from multiple sources: a domain-dependent regularization approach. IEEE Trans. Neural Netw. Learn. Syst. 23(3), 504–518 (2012)CrossRef

Duan, L., Xu, D., Tsang, I.H., Luo, J.: Visual event recognition in videos by learning from web data. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1667–1680 (2012)CrossRef

Feng, Y., Wu, X., Wang, H., Liu, J.: Multi-group adaptation for event recognition from videos. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 3915–3920. IEEE (2014)

Hammoud, R.I., Sahin, C.S., Blasch, E.P., Rhodes, B.J.: Multi-source multi-modal activity recognition in aerial video surveillance. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 237–244. IEEE (2014)

10.

Ikizler-Cinbis, N., Cinbis, R.G., Sclaroff, S.: Learning actions from the web. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 995–1002. IEEE (2009)

11.

Izadinia, H., Shah, M.: Recognizing complex events using large margin joint low-level event model. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 430–444. Springer, Heidelberg (2012) CrossRef

12.

Jia, Y.: Caffe: an open source convolutional architecture for fast feature embedding (2013). http://caffe.berkeleyvision.org

13.

Jiang, W., Zavesky, E., Chang, S.F., Loui, A.: Cross-domain learning methods for high-level visual concept classification. In: 15th IEEE International Conference on Image Processing, 2008, ICIP 2008, pp. 161–164. IEEE (2008)

14.

Jiang, Y.G., Ye, G., Chang, S.F., Ellis, D., Loui, A.C.: Consumer video understanding: a benchmark database and an evaluation of human and machine performance. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 29. ACM (2011)

15.

MED2014. http://www.nist.gov/itl/iad/mig/med14.cfm

16.

Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRef

17.

Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)

18.

Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach.In: Proceedings of the 17th International Conference on Pattern Recognition, 2004, ICPR 2004, vol. 3, pp. 32–36. IEEE (2004)

19.

Sefidgar, Y.S., Vahdat, A., Se, S., Mori, G.: Discriminative key-component modelsfor interaction detection and recognition. In: Computer Vision and Image Understanding (2015)

20.

Trichet, R., Nevatia, R.: Video segmentation descriptors for event recognition. In:2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1940–1945. IEEE (2014)

21.

Vahdat, A., Cannons, K., Mori, G., Oh, S., Kim, I.: Compositional models for videoevent detection: a multiple kernel learning latent variable approach. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 185–1192. IEEE (2013)

22.

Wang, H., Wu, X., Jia, Y.: Annotating videos from the web images. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2801–2804. IEEE(2012)

23.

Wang, H., Wu, X., Jia, Y.: Video annotation via image groups from the web. IEEE Trans. Multimed. 16, 1282–1291 (2014)CrossRef

24.

Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive SVMS. In: Proceedings of the 15th International Conference onMultimedia,pp. 188–197. ACM (2007)

Titel: A Multiple Image Group Adaptation Approach for Event Recognition in Consumer Videos
verfasst von: Dengfeng Zhang
Wei Liang
Hao Song
Zhen Dong
Xinxiao Wu
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-21977-6

Electronic ISBN: 978-3-319-21978-3

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-21978-3_16

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