Abstract
We present a video as a set of people, each person is a sequence of faces clustered by proposed hierarchical tree representation with the purpose of finding all the occurrences of a person in the video without any help of textual information. In the proposed method, faces in a video are detected and tracked to be face-tracks at first, and each face-track is associated with one person. By leveraging temporal constrains, face-tracks that depict the same person in a video are connected. Then we build undirected graphs for a video, and extend discriminative histogram intersection metric learning to generate semantic distances for cutting undirected graphs to be face clusters without predefining the number of clusters. When searching for videos containing the person of query, it is effective to compare faces of query video with sets of people summarized from videos in the dataset. Experimental results show that the proposed face clustering can improve the mean Average Precision of video retrieval and decrease the query time compared to several state-of-the-art 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
References
Zhang, Y.F., Xu, C.S., Lu, H.Q., Huang, Y.M.: Character identification in feature-length films using global face-name matching. IEEE Trans. Multimedia 11(7), 1276–1288 (2009)
Sivic, J., Everingham, M., Zisserman, A.: Person spotting: video shot retrieval for face sets. In: Leow, W.-K., Lew, M.S., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 226–236. Springer, Heidelberg (2005). https://doi.org/10.1007/11526346_26
Hao, P., Kamata, S.: Efficiently finding individuals from video dataset. IEICE Trans. Inf. Syst. E95-D(5), 1280–1287 (2012)
Nguyen, T., Ngo, T., Le, D.-D., Satoh, S., Le, B., Duong, D.: An efficient method for face retrieval from large video datasets, pp. 382–389 (2010)
Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: Proceedings of IEEE International Conference on Computer Vision Pattern Recognition (2008)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Tapaswi, M., Bauml, M., Stiefelhagen, R.: Knock! Knock! Who is it? Probabilistic person identification in TV-series. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (2012)
Sang, J., Xu, C.: Character-based movie summarization. In: Proceedings of 18th ACM International Conference on Multimedia, pp. 855–858 (2010)
Wu, B., Zhang, Y., Hu, B.-G., Ji, Q.: Constrained clustering and its application to face clustering in videos. In: Proceedings of IEEE International Conference on Computer Vision Pattern Recognition (2013)
Xiao, S., Tan, M., Xu, D.: Weighted block-sparse low rank representation for face clustering in videos. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 123–138. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_9
Hao, P., Yang, X., Li, X., Kamata, S., Chen, S.: Discriminative histogram intersection metric learning and its applications. J. Comput. Sci. Technol. 32(3), 507–519 (2017)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 511–518 (2001)
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Conference on Computer Vision, Pattern Recognition, pp. 593–600 (1994)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: Proceedings of IEEE Conference on Computer Vision, pp. 498–505 (2009)
Cherniavsky, N., Laptev, I., Sivic, J., Zisserman, A.: Semi-supervised learning of facial attributes in video. In: Kutulakos, K.N. (ed.) ECCV 2010. LNCS, vol. 6553, pp. 43–56. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35749-7_4
Cinbis, R.G., Verbeek, J., Schmid, C.: Unsupervised metric learning for face identification in TV video. In: Proceedings of IEEE Conference on Computer Vision, pp. 1559–1566 (2011)
Huang, K., Ying, Y., Campbell, C.: GSML: a unified framework for sparse metric learning. In: Proceedings of ICDM, pp. 189–198 (2009)
Sibson, R.: SLINK: an optimally efficient algorithm for the single-link cluster method. Comput. J. (British Computer Society) 16(1), 30–34 (1973)
Acknowledgments
This work was supported in part by Zhejiang Provincial Natural Science Foundation of China (LQ15F020008, LY15F020028, LQ15F030005)and National Natural Science Foundation of China (61502424, 61503338)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hao, P., Manhando, E., Bai, C., Huang, Y. (2018). Hierarchical Tree Representation Based Face Clustering for Video Retrieval. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-77383-4_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77382-7
Online ISBN: 978-3-319-77383-4
eBook Packages: Computer ScienceComputer Science (R0)