Skip to main content
SpringerLink
Log in

Automated face retrieval using bag-of-features and sigmoidal grey wolf optimization

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

A content based image retrieval system is one of the prime research fields due to the exponential increasing of multimedia data over Internet especially images. Although, a number of content based image retrieval methods have been introduced, it is still a challenging task specially for face recognition. Therefore, this work presents an automated face retrieval system using an enhanced bag-of-features framework. The bag-of-features framework has been modified by incorporating a new sigmoidal grey wolf optimization algorithm. The sigmoidal grey wolf optimization algorithm uses a sigmoid decreasing function to escape it from local optima. The efficiency of the proposed sigmoidal grey wolf optimization algorithm has been analyzed over various standard benchmark functions for average fitness values and convergence behavior. Furthermore, it has successfully been used to generate the codewords in bag-of-features framework. The modified bag-of-features has been utilized in content based image retrieval for Oracle Research Laboratory (ORL) face database. The simulation results represent that the proposed method effectively retrieves the faces as compared to other nature-inspired based methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Yi S, Lai Z, He Z, Cheung Y-M, Liu Y (2017) Joint sparse principal component analysis. Patt Recognit 61:524–536

    Article  Google Scholar 

  2. Zafeiriou S, Petrou M (2011) 2.5 d elastic graph matching. Comput Vis Image Underst 115(7):1062–1072

    Article  Google Scholar 

  3. Senaratne R, Halgamuge S, Hsu A. Face recognition by extending elastic bunch graph matching with particle swarm optimization. J Multimed 4(4)

  4. Wiskott L, Fellous J-M, Krüger N, Von Der Malsburg C (1997) Face recognition by elastic bunch graph matching. In: International conference on computer analysis of images and patterns. Springer, Berlin, pp 456–463

  5. Liu C, Wechsler H (1998) Enhanced fisher linear discriminant models for face recognition. In: Fourteenth international conference on pattern recognition, 1998. Proceedings. , Vol 2, IEEE, pp 1368–1372

  6. Lin C, Long F, Zhan Y (2017) Facial expression recognition by learning spatiotemporal features with multi-layer independent subspace analysis. In: 2017 10th international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI), IEEE, pp 1–6

  7. Lu J, Wang G, Zhou J (2017) Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans Image Process 26(8):4042–4054

    Article  MathSciNet  Google Scholar 

  8. Ding C, Tao D. Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans Patt Anal Mach Intell

  9. Matthews I, Baker S (2004) Active appearance models revisited. Int J Comput Vis 60(2):135–164

    Article  Google Scholar 

  10. Besbas W, Artemi M, Salman R (2014) Content based image retrieval (cbir) of face sketch images using wht transform domain. Inf Environ Energy Appl 66:77–81

    Google Scholar 

  11. Shih P, Liu C (2005) Comparative assessment of content-based face image retrieval in different color spaces. Int J Patt Recognit Artif Intell 19(07):873–893

    Article  Google Scholar 

  12. ElAdel A, Ejbali R, Zaied M, Amar CB (2016) A hybrid approach for content-based image retrieval based on fast beta wavelet network and fuzzy decision support system. Mach Vis Appl 27(6):781–799

    Article  Google Scholar 

  13. Desai R, Sonawane B (2017) Gist, hog, and dwt-based content-based image retrieval for facial images. In: Proceedings of the international conference on data engineering and communication technology. Springer, Berlin, pp 297–307

  14. Sultana M, Gavrilova ML (2014) Face recognition using multiple content-based image features for biometric security applications. Int J Biometr 6(4):414–434

    Article  Google Scholar 

  15. Wang X-Y, Liang L-L, Li Y-W, Yang H-Y (2017) Image retrieval based on exponent moments descriptor and localized angular phase histogram. Multimed Tools Appl 76(6):7633–7659

    Article  Google Scholar 

  16. Wu Z, Ke Q, Sun J, Shum H-Y (2010) Scalable face image retrieval with identity-based quantization and multi-reference re-ranking. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, pp 3469–3476

  17. Saraswat M, Arya K (2014) Feature selection and classification of leukocytes using random forest. Med Biol Eng Comput 52:1041–1052

    Article  Google Scholar 

  18. Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2016) Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35(1):119–130

    Article  Google Scholar 

  19. Chang H, Nayak N, Spellman PT, Parvin B (2013) Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 91–98

  20. Cruz-Roa AA, Ovalle JEA, Madabhushi A, Osorio FAG (2013) A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 403–410

  21. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  22. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1, IEEE, pp 886–893

  23. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Patt Recognit 29(1):51–59

    Article  Google Scholar 

  24. Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, vol 1, Prague, pp 1–2

  25. Hussain K, Salleh MNM, Cheng S, Shi Y (2018) Metaheuristic research: a comprehensive survey. Artif Intell Rev 1–43

  26. Saraswat M, Arya K, Sharma H (2013) Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm Evol Comput 11:46–54

    Article  Google Scholar 

  27. Reference details to be updated.

  28. Chhikara RR, Sharma P, Singh L (2016) A hybrid feature selection approach based on improved pso and filter approaches for image steganalysis. Int J Mach Learn Cybern 7:1195–1206

    Article  Google Scholar 

  29. Mohammadi FG, Abadeh MS (2014) Image steganalysis using a bee colony based feature selection algorithm. Eng Appl Artif Intell 31:35–43

    Article  Google Scholar 

  30. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  Google Scholar 

  31. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  32. Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput 2016:8

    Google Scholar 

  33. Long W, Liang X, Cai S, Jiao J, Zhang W (2017) A modified augmented lagrangian with improved grey wolf optimization to constrained optimization problems. Neural Comput Appl 28(1):421–438

    Article  Google Scholar 

  34. Rodríguez L, Castillo O, Soria J (2016) Grey wolf optimizer with dynamic adaptation of parameters using fuzzy logic. In: 2016 IEEE congress on evolutionary computation (CEC), IEEE, pp 3116–3123

  35. Dudani K, Chudasama A (2016) Partial discharge detection in transformer using adaptive grey wolf optimizer based acoustic emission technique. Cogent Eng 3(1):1256083

    Article  Google Scholar 

  36. Malik MRS, Mohideen ER, Ali L (2015) Weighted distance grey wolf optimizer for global optimization problems. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC), IEEE, pp 1–6

  37. Zhang S, Zhou Y (2015) Grey wolf optimizer based on powell local optimization method for clustering analysis. Discrete Dyn Nat Soc

  38. Muangkote N, Sunat K, Chiewchanwattana S (2014) An improved grey wolf optimizer for training q-gaussian radial basis functional-link nets. In: Proceedings of the international conference on computer science and engineering, pp 209–214

  39. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  40. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  41. Kumar S, Sharma B, Sharma VK, Sharma H, Bansal JC Plant leaf disease identification using exponential spider monkey optimization. Sustain Comput Inf Syst

  42. Sharma K, Chhamunya V, Gupta P, Sharma H, Bansal JC (2015) Fitness based particle swarm optimization. Int J Syst Assur Eng Manag 6(3):319–329

    Article  Google Scholar 

  43. Mittal H, Pal R, Kulhari A, Saraswat M (2016) Chaotic kbest gravitational search algorithm (ckgsa). In: 2016 ninth international conference on contemporary computing (IC3), IEEE, pp 1–6

  44. Khandelwal A, Bhargava A, Sharma A, Sharma H (2018) Modified grey wolf optimization algorithm for transmission network expansion planning problem. Arab J Sci Eng 43(6):2899–2908

    Article  Google Scholar 

  45. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3–18

    Article  Google Scholar 

  46. ali Bagheri M, Montazer GA, Escalera S (2012) Error correcting output codes for multiclass classification: application to two image vision problems. In: 2012 16th CSI international symposium on artificial intelligence and signal processing (AISP), IEEE, pp 508–513

  47. Jiang Y-G, Yang J, Ngo C-W, Hauptmann AG (2010) Representations of keypoint-based semantic concept detection: a comprehensive study. IEEE Trans Multimed 12(1):42–53

    Article  Google Scholar 

  48. Orl database of face images. https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html (September 2018)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Allahabad Campus, Allahabad, India

    Arun Kumar Shukla & Suvendu Kanungo

Authors
  1. Arun Kumar Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suvendu Kanungo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Kumar Shukla.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shukla, A.K., Kanungo, S. Automated face retrieval using bag-of-features and sigmoidal grey wolf optimization. Evol. Intel. 14, 1201–1212 (2021). https://doi.org/10.1007/s12065-019-00213-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00213-w

Keywords

Search

Navigation