Constrained Spectral Clustering on Face Annotation System

  • Jiajie Han
  • Jiani Hu
  • Weihong Deng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 662)


Face clustering is a common feature in face annotation system like intelligent photo albums and photo management systems. But unsupervised clustering algorithms perform poorly and researchers turn to work with constrained clustering algorithms that take the user interactions as constraints. Mostly, the constraints are pairwise constraints in the form of Must-Link or Cannot-Link, which can be easily integrated in spectral clustering algorithm. In this paper, we propose a design of face annotation system that can generate more informative constraints and better use constraints with constrained spectral clustering. And we examine the system in a lab situation dataset and a real-live dataset, of which results demonstrate the effectiveness of our method.


Face clustering Spectral clustering Constrained clustering User interactions Pairwise constraints 



This work was partially sponsored by supported by the NSFC (National Natural Science Foundation of China) under Grant No. 61375031, No. 61573068, No. 61471048, and No.61273217, the Fundamental Research Funds for the Central Universities under Grant No. 2014ZD03-01, This work was also supported by Beijing Nova Program, CCF-Tencent Open Research Fund, and the Program for New Century Excellent Talents in University.


  1. 1.
    Cohn, D., Caruana, R., McCallum, A.: Semi-supervised clustering with user feedback. Constrained Clustering: Adv. Algorithms Theor. Appl. 4(1), 17–32 (2003)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Cui, J., Wen, F., Xiao, R., Tian, Y., Tang, X.: EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 367–376. ACM (2007)Google Scholar
  3. 3.
    Wagstaff, K., Cardie, C., Rogers, S., Schrodl, S.: Constrained k-means clustering with background knowledge. In: ICML, vol. 1, pp. 577–584 (2001)Google Scholar
  4. 4.
    Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proceedings of 19th International Conference on Machine Learning (2002)Google Scholar
  5. 5.
    Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the twenty-first international conference on Machine learning, p. 11. ACM (2004)Google Scholar
  6. 6.
    Lelis, L., Sander, J.: Semi-supervised density-based clustering. In: Ninth IEEE International Conference Data Mining, ICDM 2009, pp. 842–847. IEEE (2009)Google Scholar
  7. 7.
    Ruiz, C., Spiliopoulou, M., Menasalvas, E.: C-DBSCAN: density-based clustering with constraints. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS (LNAI), vol. 4482, pp. 216–223. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Kamvar, K., Sepandar, S., Klein, K., Dan, D., Manning, M., Christopher, C.: Spectral learning. In: International Joint Conference of Artificial Intelligence. Stanford InfoLab (2003)Google Scholar
  9. 9.
    Xiong, C., Johnson, D., Corso, J.J.: Active clustering with model-based uncertainty reduction. arXiv preprint arXiv:1402.1783 (2014)
  10. 10.
    Wang, X., Davidson, I.: Flexible constrained spectral clustering. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 563–572. ACM (2010)Google Scholar
  11. 11.
    Xu, L., Li, W., Schuurmans, D.: Fast normalized cut with linear constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2866–2873. IEEE (2009)Google Scholar
  12. 12.
    Li, Z., Liu, J., Tang, X.: Constrained clustering via spectral regularization. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 421–428. IEEE (2009)Google Scholar
  13. 13.
    Rangapuram, S.S., Hein, M.: Constrained 1-spectral clustering. arXiv preprint arXiv:1505.06485 (2015)
  14. 14.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2, 849–856 (2002)Google Scholar
  15. 15.
    Voiron, N., Benoit, A., Filip, A., Lambert, P., Ionescu, B.: Semi-supervised spectral clustering with automatic propagation of pairwise constraints. In: 2015 13th International Workshop Content-Based Multimedia Indexing (CBMI), pp. 1–6. IEEE (2015)Google Scholar
  16. 16.
    Davidson, I., Wagstaff, K.L., Basu, S.: Measuring constraint-set utility for partitional clustering algorithms. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS, vol. 4213, pp. 115–126. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  18. 18.
    Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision. IEEE, pp. 138–142 (1994)Google Scholar
  19. 19.
    Xiong, X., Torre, F.: Supervised descent method and its applications to face alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 532–539 (2013)Google Scholar
  20. 20.
    Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 267–273. ACM (2003)Google Scholar
  21. 21.
    Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations