Skip to main content

Safe binary particle swam algorithm for an enhanced unsupervised label refinement in automatic face annotation


Mining web facial images on the internet has become as a profitable and important paradigm towards auto face annotation technique. The unsupervised label refinement (ULR) is an effective method that can fix weakly labeled facial images data which are collected from the internet and included some images with wrong label. In order to improve the correction accuracy of ULR, particle swarm optimization (PSO) and binary particle swarm optimization (BPSO) are used for solving binary constraint optimization task in this study. A novel method named safe binary particle swam optimization (SBPSO) is also proposed to improve BPSO which has the probability over range problem for using the ULR. In addition, SBPSO is also employed for an enhanced ULR (EULR) objective function which is created by modifying the original formula of ULR to improve the accuracy of labeled facial image. An experimental database is queried from IMDb website which collected the actors who were bored in 1950 to 1990. Some error flags are randomly added in the database for the correction tests by different methods. The results showed that the SBPSO Algorithm for the EULR in automatic face annotation have the better label correction rate and convergence effect.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. Asthana A, Lucey S, Goecke R (2011) Regression based automatic face annotation for deformable model building. Pattern Recogn 44(10–11):2598–2613

    Article  MATH  Google Scholar 

  2. Berg TL, Berg AC, Edwards J, Maire M, White R, Yee-Whye T et al (2004) Names and faces in the news. In: Proceedings of the 2004 I.E Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, volume 2, pp 848–854

  3. Bu J, Xu B, Wu C, Chen C, Zhu J, Cai D et al (2012) Unsupervised face-name association via commute distance. In: Proceedings of the 20th ACM international conference on Multimedia, pp 219–228

  4. Carneiro G, Chan AB, Moreno PJ, Vasconcelos N (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans Pattern Anal Mach Intell 29:394–410

    Article  Google Scholar 

  5. Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. IEEE Trans Neural Netw 20(3)

  6. Dayong W, Hoi SCH, Ying H, Jianke Z (2014a) Mining weakly labeled web facial images for search-based face annotation. IEEE Trans Knowl Data Eng 26(1):166–179

    Article  Google Scholar 

  7. Dayong W, Hoi SCH, Ying H, Jianke Z, Tao M, Jiebo L (2014b) Retrieval-based face annotation by weak label regularized local coordinate coding. IEEE Trans Pattern Anal Mach Intell 36(3):550–563

    Article  Google Scholar 

  8. Docherty P, Chase JG, David T (2012a) Characterisation of the iterative integral parameter identification method. Med Biol Eng Comput 50(2):127–1e34

    Article  Google Scholar 

  9. Docherty PD, Schranz C, Chase JG, Chiew YS, Möller K (2012b) Traversing the Fuzzy Valley: problems caused by reliance on default simulation and parameter identification programs for discontinuous models. IFAC Proc Vol 45(18):490–494

    Article  Google Scholar 

  10. Docherty PD, Schranz C, Chase JG, Chiew YS, Möller K (2014) Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: a case-study of pulmonary mechanics. Comput Methods Prog Biomed 114(3):e70–e78

    Article  Google Scholar 

  11. Duygulu P, Barnard K, de Freitas JF, and Forsyth DA (2002) Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Computer Vision—ECCV 2002 on 2353, pp 97–112

  12. Esmin A, Aoki A, Lambert-Torres G (2002) Particle swarm optimization for fuzzy membership functions optimization. In: International Conference on Systems, Man and Cybernetics, 2002 I.E, volume 3, p 6

  13. Fan J, Gao Y, Luo H (2004) Multi-level annotation of natural scenes using dominant image components and semantic concepts. In: Proceedings of the 12th annual ACM international conference on Multimedia, pp 540–547

  14. Hoi SCH, Wang D, Cheng IY, Lin EW, Zhu J, He Y, et al (2013) FANS: face annotation by searching large-scale web facial images. In: Proceedings of the 22nd international conference on World Wide Web, pp 317–320

  15. Hu X, Eberhart RC, Shi Y (2003) Swarm intelligence for permutation optimization: a case study of n-queens problem. In: Proceedings of the 2003 I.E. on Swarm Intelligence Symposium, 2003. SIS'03, pp 243–246

  16. Kaufman L, Rousseeuw P (1987) Clustering by means of medoids: North-Holland. In: Dodge Y (ed) Statistical data analysis based on the L1 norm and related methods. North Holland/Elsevier on, Amsterdam, pp. 405–416

    Google Scholar 

  17. Khanesar MA, Teshnehlab M, Shoorehdeli MA (2007) A novel binary particle swarm optimization. In: Mediterranean Conference on Control & Automation, 2007. MED'07, pp 1–6

  18. Luo Z-Q, Tseng P (1993) On the convergence rate of dual ascent methods for linearly constrained convex minimization. Math Oper Res 18(4):846–867

    MathSciNet  Article  MATH  Google Scholar 

  19. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp 281–297

  20. Mao Q, Tsang IW-H, Gao S (2013) Objective-guided image annotation. IEEE Trans Image Process 22(4):1585–1597

    MathSciNet  Article  Google Scholar 

  21. Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: bringing order to the web. Technical Report, Stanford InfoLab

  22. Pang L, Ngo C-W (2015) Unsupervised celebrity face naming in web videos. IEEE Trans Multimedia 17(6):854–866

    Article  Google Scholar 

  23. Paul E, Ajeena Beegom AS (2015) Mining Images for Image Annotation using SURF Detection Technique. 2015 International Conference on Control Communication & Computing India (ICCC), pp 724–728

  24. Pham PT, Moens M-F, Tuytelaars T (2010) Naming persons in news video with label propagation. In: International Conference on Multimedia and Expo (ICME), 2010 I.E., pp 1528–1533

  25. Salerno J (1997) Using the particle swarm optimization technique to train a recurrent neural model. Proceedings. Ninth IEEE International Conference on Tools with Artificial Intelligence, 1997, pp 45–49

  26. Satoh S, Nakamura Y, Kanade T (1999) Name-it: naming and detecting faces in news videos. IEEE MultiMedia 6(1):22–35

    Article  Google Scholar 

  27. Shi Y and Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 I.E. International Conference on Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, pp 69–73

  28. Siagian C, Itti L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29(2):300–312

    Article  Google Scholar 

  29. Sousa T, Silva A, Neves A (2004) Particle swarm based data mining algorithms for classification tasks. Parallel Comput 30(5–6):767–783

    Article  Google Scholar 

  30. Tseng P (2001) Convergence of a block coordinate descent method for nondifferentiable minimization. J Optim Theory Appl 109(3):475–494

    MathSciNet  Article  MATH  Google Scholar 

  31. Wang K-P, Huang L, Zhou C-G, Pang W (2003) Particle swarm optimization for traveling salesman problem. In: 2003 International Conference on Machine Learning and Cybernetics, pp 1583–1585

  32. Wang C, Jing F, Zhang L, Zhang H-J (2006) Image annotation refinement using random walk with restarts. In: Proceedings of the 14th annual ACM international conference on Multimedia, pp 647–650

  33. Wang D, Hoi SCH, He Y (2012) A unified learning framework for auto face annotation by mining web facial images. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp 1392–1401

  34. Wang D, Hoi SCH, Wu P, Zhu J, He Y, Miao C (2013) Learning to name faces: a multimodal learning scheme for search-based face annotation. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp 443–452

  35. Yang J, Hauptmann AG (2004) Naming every individual in news video monologues. In: Proceedings of the 12th annual ACM international conference on Multimedia, pp 580–587

  36. Zhou Y, Jin R, Hoi S (2010) Exclusive lasso for multi-task feature selection. In: JMLR Workshop and Conference Proceedings: 13th International Conference on Artificial Intelligence and Statistics, volume 9, pp 988–995

  37. Zhu J, Hoi SC, Lyu MR (2008) Face annotation using transductive kernel fisher discriminant. IEEE Trans Multimedia 10(1):86–96

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jieh-Ren Chang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chang, JR., Juang, HC., Chen, YS. et al. Safe binary particle swam algorithm for an enhanced unsupervised label refinement in automatic face annotation. Multimed Tools Appl 76, 18339–18359 (2017).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Particle swam algorithm
  • Automatic face annotation
  • Unsupervised label refinement