Skip to main content
SpringerLink
Log in

Object recognition using cognition based decision tree clustering in multi-level artificial neural network classifier and self similarity as feature criterion

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This work showed the capability of handling large number of classes for classification with human cognition inspired methods. A cognition based techniques for both feature extraction, (self-similarity feature, Intensity Level Multi Fractal Dimension (ILMFD)) as well as classification purpose (decision tree clustering based multi-level Artificial Neural Network classifier-MLANN-DTC) were employed to implement facial recognition based object detection system. A DTC based approach reduces the search space time and also provides opportunity for very less amount of classes (a smaller part of the large number of classes) to be handled by the respective classifier for classification. It also mimics fast recognition capability of humans. In this work, two different databases were used for experiment, first one is our own collected facial images from rotation based video clips (117 persons and 40 facial images per person) named as NS database, and other is standard ORL database (40 persons and 10 facial images per person). In pre-processing step, the facial images were segmented to obtain facial part using context window based texture of pixels (CWTP) & back-propagation neural network (BPNN) based model and then a scale and rotation independent ILMFD feature was computed from each segmented image. Further, a combination of K-means and hierarchal clustering was used to build super classes. All classes’ data were distributed among these 6 super classes (heuristically chosen) for own NS database and 3 for ORL database as per their similarity based on ILMFD features. Multi-level ANNs models were employed for all super classes and further their classification results were fed into decision clustering based model to obtain fine-tuned results, which showed significant improvement in terms of classification efficiency. This approach believes in center tendency of largest cluster to refer the actual class decision from multiple decisions obtain corresponding to multiple input data of the same class. In this work, the MLANN-DTC based proposed model has produced 89.542 ± 1.167% and 87.098 ± 2.066% classification efficiency (± standard deviation) for single input and for group based decision (decision clustering), 95.042 ± 0.719% and 89 ± 2.549% for NS and ORL database respectively. This improved classification results motivate its application for other object recognition and classification problems. The basic idea of this work also supports better handling of classification which deals with a large number of classes.

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
Fig. 7

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ANN:

Artificial Neural Network

BPNN:

Back-Propagation Neural Network

CWTP:

Context Window Based Texture Of Pixels

DTC:

Decision Tree Clustering

FD:

Fractal Dimension

ILMFD:

Intensity-Level Multi-Fractal Dimension

MLANN:

Multi-level Artificial Neural Network

MRI:

Magnetic Resonance Imaging

NS:

Non-standard Database

ORL:

Olivetti Research Laboratory

PRC:

Pixel Range Calculation

SEM:

Scanning Electron Microscope

References

  1. Khan MNA, Qureshi SA, Riaz N (2013) Gender classification with decision trees. Int J Signal Process Image Process Pattern Recognit 6(1):165–176. https://doi.org/10.1109/CGiV.2016.33

    Article  Google Scholar 

  2. Karthigayani P, Sridhar S (2014) Decision tree based occlusion detection in face recognition and estimation of human age using back propagation neural network. J Comput Sci 10(1):115–127. https://doi.org/10.3844/jcssp.2014.115.127

    Article  Google Scholar 

  3. Ren N, Zargham M, Rahimi S (2006) A decision tree-based classification approach to rule extraction for security analysis. Int J Inf Technol Decis Mak 5(1):227–240

    Article  Google Scholar 

  4. Salmam FZ, Madani A, Kissi M (2016) Facial Expression recognition using decision trees. 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), pp 125–130. https://doi.org/10.1109/CGiV.2016.33

  5. Tsai CA, Chen DT, Chen JJ, Balch CM, Thompson JF, Soong SJ (2007) An integrated tree-based classification approach to prognostic grouping with application to localized melanoma patients. J Biopharm Stat 17(3):445–460. https://doi.org/10.1080/10543400701199585

    Article  MathSciNet  PubMed  Google Scholar 

  6. Pepik B, Kalajdziski S, Davcev D (2009) Protein classification using decision trees with bottom-up classification approach. 13th International Conference on Biomedical Engineering, Proceeding 23, pp 174–178

  7. Muqasqas SA, Radaideh QAA, Abul-Huda BA (2014) A hybrid classification approach based on decision tree and naïve bays methods. Int J Inf Retr Res 4(4):61–72. https://doi.org/10.4018/IJIRR.2014100104

    Article  Google Scholar 

  8. Wilson CL, Grother PJ, Barnes CS (1996) Binary decision clustering for neural-network based optical character recognition. Pattern Recogn 29(3):425–437

    Article  ADS  Google Scholar 

  9. Ebrahimpour R, Ehteram SR, Kabir E (2005) Face recognition by multiple classifiers, a divide-and-conquer approach, knowledge-based intelligent information and engineering systems. Lecture notes in computer science. Springer, 3683, pp 225–232

  10. Wallis GM, Bülthoff HH (2001) Effects of temporal association on recognition memory. Proc Natl Acad Sci U S A 98(8):4800–4804

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bruce V, Young A (1998) In the eye of the beholder. The science of face perception. Oxford University Press Inc., New York

    Google Scholar 

  12. Knight B, Johnston A (1997) The role of movement in face recognition. Vis Cogn 4(3):265–274

    Article  Google Scholar 

  13. Jiang J, Zhang J, Yang G, Zhang D, Zhang L (2010) Application of back propagation neural network in the classification of high resolution remote sensing image: take remote sensing image of Beijing for instance. In: Proceedings of 18th International Conference on Geoinformatics, IEEE, pp 1–6

  14. Atkinson P, Tatnall A (1997) Introduction neural networks in remote sensing. Int J Remote Sens 18(4):699–709

    Article  Google Scholar 

  15. Alsmadi M, Omar K, Noah S (2009) Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. IJCSNS Int J Comput Sci Netw Secur 9(4):378–383

    Google Scholar 

  16. Ebrahimpour-Komleh H, Chandran V, Sridharan S (2001) Robustness to expression variations in fractal-based face recognition. Sixth International, Symposium on Signal Processing and its Applications, Kuala Lumpur, August 13–16, 2001, vol 1, pp 359–362

  17. Kaewchinporn C (2011) A combination of decision tree learning and clustering for data classification. Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE), Nakhon Pathom, May 11–13, 2011, pp 363–367

  18. Kumar U, Lahiri T (2013) Significant enhancement of object recognition efficiency using human cognition based decision clustering. Int J Comput Vis Image Process 3(4):1–15

    Google Scholar 

  19. Tripathi E, Kumar U, Tripathi SP (2022) Image splicing detection system using intensity-level multi-fractal dimension feature engineering and twin support vector machine based classifier. Multimed Tools Appl 10:14. https://doi.org/10.1007/s11042-022-13519-2. ISSN: 13807501

    Article  Google Scholar 

  20. Chaudhuri BB, Sarkar N (1995) Texture segmentation using fractal dimension. IEEE Trans Pattern Anal Mach Intell 17:72–77. https://doi.org/10.1109/34.368149

    Article  Google Scholar 

  21. Mandelbrot BB, Wheeler JA (1983) The fractal geometry of nature. Am J Phys 51(3):286–287. https://doi.org/10.1119/1.13295

    Article  ADS  Google Scholar 

  22. Shanmugavadivu P, Sivakumar V (2012) Fractal dimension based texture analysis of digital images. Procedia Eng 38:2981–2986. https://doi.org/10.1016/j.proeng.2012.06.348. ISSN 1877-7058

    Article  Google Scholar 

  23. Keller J, Crownover R, Chen S (1989) Texture description and segmentation through fractal geometry. Comput Vis Graph Image Process 45:150–160. https://doi.org/10.1016/0734-189x(89)90130-8

    Article  Google Scholar 

  24. Ranganath A, Mishra J (2017) New approach for estimating fractal dimension of both gary and color images. In: IEEE 7th International Advance Computing Conference (IACC), Hyderabad, pp 678–683. https://doi.org/10.1109/iacc.2017.0142

  25. Ghaderi S (2023) Fractal dimension image processing for feature extraction and morphological analysis: Gd3+/13X/DOX/FA MRI nanocomposite. J Nanomater 2023:Article ID 8564161, 11 pages. https://doi.org/10.1155/2023/8564161

  26. Ranganath A, Senapati MR, Sahu PK (2021) Estimating the fractal dimension of images using pixel range calculation technique. Vis Comput 37:635–650. https://doi.org/10.1007/s00371-020-01829-1

    Article  Google Scholar 

  27. Tan T, Yan H (1999) Face recognition by fractal transformations. Proceedings, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, March 15–19, 1999, vol 6, pp 3537–3540

  28. Hossein EKV, Chandran V, Sridharan S (2001) Face recognition using fractal codes. Conference: Image Processing Proceedings. 2001 International Conference on Volume: 3. https://doi.org/10.1109/ICIP.2001.958050

  29. Kouzani AZ, He F, Sammut K (1997) Fractal faces representation and recognition. IEEE International Conference on Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation, Orlando, FL, October 12–15, vol 2, pp 1609–1613

  30. Ferens K, Kinsner W (1995) Multifractal texture classification of images. IEEE proceedings, conference on communication, power, and computing, vol 2, pp 438–444

  31. Deaton R et al (1994) Fractal analysis of magnetic resonance images of the brain, proceedings of the 16th annual international conference of the ieee engineering in medicine and biology society. Engineering advances: new opportunities for biomedical engineers (cat. no.94ch3474-4), proceedings of 16th annual international conferenc, 1994, new york, ny, usa, ieee, usa, pp 616–617 vol.1, xp000965329, isbn: 0-7803-2050-6

  32. Cheng SC, Yueh-Min H (2003) A novel approach to diagnose diabetes based on the fractal characteristics of retinal images. IEEE Trans Inf Technol Biomed 7(3):163–170

    Article  PubMed  Google Scholar 

  33. Liu D, Teng W (2022) Deep learning-based image target detection and recognition of fractal feature fusion for Biometric authentication and monitoring. Net Model Anal Health Inform Bioinform 11(1). https://doi.org/10.1007/s13721-022-00355-5

  34. Bisogni C, Nappi M, Pero C, Ricciardi S (2020) HP2IFS: head pose estimation exploiting partitioned iterated function systems. In: 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp 1725–1730. https://doi.org/10.1109/ICPR48806.2021.9413227

  35. Abdullahi SM, Wang H, Li T (2020) Fractal coding-based robust and alignment-free fingerprint image hashing. IEEE Trans Inf Forensics Secur 15:2587–2601. https://doi.org/10.1109/TIFS.2020.2971142

    Article  Google Scholar 

  36. The ORL Database of Faces. https://www.kaggle.com/datasets/kasikrit/att-database-of-faces

  37. Kumar U, Lahiri T (2013) Segmentation of ill-defined objects by convoluting context window of each pixel with a non-parametric function. Int J Comput Vis Image Process 3(1):33–41. https://doi.org/10.4018/ijcvip.2013010103

    Article  MathSciNet  Google Scholar 

  38. Schmidhuber J (1998) Facial beauty and fractal geometry. Technical Report TR IDSIA-28-98, IDSIA (1998), Published in the Cogprint Archive: http://cogprints.soton.ac.uk

  39. Xie HA, Wang JA, Stein E (1998) Direct fractal measurement and multifractal properties of fracture surfaces. Physics Letter A 242(1–2):41–50

    Article  ADS  CAS  Google Scholar 

  40. Feder J (1989) Fractals. Plenum Press, New York

    Google Scholar 

  41. Li HQ, Chen SH, Zhao HM (1991) Fat fractal and multifractals for protein and enzyme surfaces. Int J Biol Macromol 13(4):210–216

    Article  CAS  PubMed  Google Scholar 

  42. Singh R, Samal S, Lahiri T (2005) A novel strategy for designing efficient multiple classifier. Lect Notes Comput Sci 3832:713–720

    Article  Google Scholar 

  43. Aisbett J, Gibbon G (1999) Cognitive classification. 99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, Orlando, Florida, USA, pp 100–107

  44. Lafond D, Vallières BR, Vachon F, St-Louis M-E, Tremblay S (2016) Capturing non-linear judgment policies using decision tree models of classification behavior. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 831–835

  45. Luan S, Schooler LJ, Gigerenzer G (2011) A signal detection analysis of fast-and-frugal trees. Psychol Rev 118:316–338. https://doi.org/10.1037/a0022684

    Article  PubMed  Google Scholar 

  46. Lycan WG (1999) Mind and cognition: an anthology, 2nd edn. Blackwell Publishers Inc., Malden

    Google Scholar 

Download references

Acknowledgements

The authors would like to give thanks to the owners of the ORL facial database that was used in this paper for comparative work and also thanks to Prof. (Dr.) Tapobrata Lahiri, Professor, IIIT Allahabad, India for giving valuable suggestions during study of this paper’s work.

Author information

Authors and Affiliations

  1. Institute of Engineering and Technology, Lucknow, India

    Upendra Kumar

  2. Dr. A. P. J. Abdul Kalam Technical University, Uttar Pradesh, Lucknow, India

    Upendra Kumar

Authors
  1. Upendra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Upendra Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, U. Object recognition using cognition based decision tree clustering in multi-level artificial neural network classifier and self similarity as feature criterion. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18691-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18691-1

Keywords

Search

Navigation