Skip to main content
SpringerLink
Log in

CUDA-based parallelization of a bio-inspired model for fast object classification

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The need for highly accurate classification systems capable of working on real-time applications has increased in recent years. Nowadays, several computer vision tasks apply a classification step as part of bigger systems, hence requiring classification models that work at a fast pace. This rendered interesting the concept of real-time object classification to several research communities. In this paper, we propose to accelerate a bio-inspired model for object classification, which has given very good results when compared with other state-of-the-art proposals using the compute unified device architecture (CUDA) and exploiting computational capabilities of graphic processing units. The classification model that is used is called the artificial visual cortex, a novel bio-inspired approach for image classification. In this work, we show that through an implementation of this model in the CUDA framework it is possible to achieve real-time functionality. As a result, the proposed system is able to process images in average of up to 90 times faster than the original system.

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
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Hsuan L, Chih-Yin L, Chih-Jui L, Chien-Feng H, Ri-Wei D, Tzuu-Hseng S (2014) Implementation of real-time object recognition system for home-service robot by integrating SURF and BRISK. IEEE international conference on system science and engineering (ICSSE), pp 273–278

  2. Muller UA, Jackel LD, LeCun Y, Flepp B (2013) Real-time adaptive off-road vehicle navigation and terrain classification. Proc SPIE, Unmanned Syst Technol XV 8741:87410A–1

    Article  Google Scholar 

  3. Salas-Moreno RF, Newcombe RA, Strasdat H, Kelly PHJ, Davison AJ (2013) SLAM++: simultaneous localisation and mapping at the level of objects. IEEE conference on computer vision and pattern recognition (CVPR) 1352(1359):23–28

  4. Zhang X, Yang Y-H, Han Z, Wang H, Gao C (2013) Object class detection: a survey. ACM Comput Surv 46(1, 10):53

    Google Scholar 

  5. Olague G (2016) Evolutionary computer vision: the first footprints. Springer, Berlin, p 411

    Book  Google Scholar 

  6. Dozal L, Olague G, Clemente E, Hernández DE (2014) Brain programming for the evolution of an artificial dorsal stream. Cognit Comput 6(3):528–557

    Article  Google Scholar 

  7. Hernández DE, Clemente E, Olague G, Briseño JL (2016) Evolutionary multi-objective visual cortex for object classification in natural images. J Comput Sci 17(1):216–233

    Article  Google Scholar 

  8. Malik ZK, Hussain A, Wu J (2014) Novel biologically inspired approaches to extracting online information from temporal data. Cognit Comput 6(3):595–607

    Article  Google Scholar 

  9. Yang Y, Wu QMJ, Wang Y, Zeeshan KM, Lin X, Yuan X (2014) Data partition learning with multiple extreme learning machines. IEEE Trans Cybern 45(8):1463–1475

    Article  Google Scholar 

  10. Ng LT, Suandi SA, Teoh SS (2014) Vehicle classification using visual background extractor and multi-class support vector machines. The 8th international conference on robotic, vision, signal processing & power applications. Lecture notes in electrical engineering, vol 291, pp 221–227

    Google Scholar 

  11. Asano S, Maruyama T, Yamaguchi Y (2009) Performance comparison of FPGA, GPU and CPU in image processing. International conference on field programmable logic and applications. FPL, pp 126–131

  12. Lu M (2013) Fast implementation of scale invariant feature transform based on CUDA. Appl Math Inf Sci 7(2):717–722

    Article  Google Scholar 

  13. Chase J, Nelson B, Bodily J, Wei Z, Lee D (2008) Real-time optical flow calculations on FPGA and GPU architectures: a comparison study. 16th international symposium on field-programmable custom computing machines. FCCM ’08, pp 173–182

  14. Jiang S, Wang Y, Chen Z, Sun K (2014) Real-time brain extraction method from cerebral MRI volume based on graphic processing units. Neural Comput Appl 25(5):1145–1151

    Article  Google Scholar 

  15. Ohmer J, Maire F, Brown R (2006) Implementation of kernel methods on the GPU. Digital image computing: techniques and applications, DICTA 05. Queensland, Australia, pp 1–8

  16. Uetz R, Behnke S (2009) Large-scale object recognition with CUDA-accelerated hierarchical neural networks. IEEE international conference on intelligent computing and intelligent systems, ICIS 09, pp 1–6

  17. Chikkerur S (2008) CUDA implementation of a biologically inspired object recognition system. MIT technical report

  18. Mutch J, Knoblich U, Poggio T (2010) CNS: a GPU-based framework for simulating cortically-organized networks. MIT technical report

  19. Parks D, Jain A, McInerney J, Itti L (2010) GPGPU-based real-time object detection and recognition system. J Vis 10(7):997

    Article  Google Scholar 

  20. Woodbeck K, Roth G, Huiqiong C (2008) Visual cortex on the GPU: biologically inspired classifier and feature descriptor for rapid recognition. IEEE computer society conference on computer vision and pattern recognition workshops. CVPRW ’08, pp 1–8

  21. Olague G, Clemente E, Dozal L (2014) Evolving an artificial visual cortex for object recognition with brain programming. EVOLVE—A bridge between probability set oriented numeric and evolutionary computation III, studies in computational intelligence, Springer, vol 500, pp 97–119

  22. LeCun Y, Bottou L, Orr GB, Müller KR (1998) Efficient backprop. Neural Netw Tricks Trade LNCS 1524:9–50

    Article  Google Scholar 

  23. Bouvrie J (2006) Notes on convolutional neural networks. Center for biological and computational learning, MIT tutorial

  24. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  25. Ciresan D, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. IJCAI’11 proceedings of the twenty-second international joint conference on artificial intelligence, vol 2, pp 1237 –1242

  26. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(10):1929–1958

    MathSciNet  MATH  Google Scholar 

  27. Dhanasekaran B, Rubin N (2011) A new method for GPU based irregular reductions and its application to K-means clustering. In: Proceedings of the fourth workshop on general purpose processing on graphics processing units, vol 2, pp 2- 8

  28. Xiao Y, Leung CS, Ho T, Lam P (2011) A GPU implementation for LBG and SOM training. Neural Comput Appl 20(7):1035–1042

    Article  Google Scholar 

  29. Nantes A, Brown R, Maire F (2013) Neural network-based detection of virtual environment anomalies. Neural Comput Appl 23(6):1711–1728

    Article  Google Scholar 

  30. Milner AD, Goodale MA (2006) The visual brain in action, 2nd edn. Oxford University Press, Oxford, p 297

    Book  Google Scholar 

  31. Clemente E, Chavez F, Fernandez de Vega F, Olague G (2015) Self-adjusting focus of attention in combination with a genetic fuzzy system for improving a laser environment control device system. Appl Soft Comput 32:250–265

    Article  Google Scholar 

  32. Olesen SM, Lyder S (2010) Applying 2D filters using GPU’s and CUDA. Vision 3—advanced topics in computer vision

  33. Sanders J, Kandrot E (2010) CUDA by example: an introduction to general-purpose gpu programming. Addison-Wesley, Boston, p 290

    Google Scholar 

  34. Podlozhnyuk V (2007) Image convolution with CUDA. NVIDIA corporation technical report, pp 1–21

  35. Huang Y, Huang K, Tao D, Tan T, Li X (2011) Enhanced biologically inspired model for object recognition. IEEE Trans Syst Man Cybern Part B 41(6):1668–1680

    Article  Google Scholar 

  36. Opelt A, Pinz A, Fussenegger M, Auer P (2006) Generic object recognition with boosting. IEEE Trans Pattern Anal Mach Intell 28(3):416–431

    Article  Google Scholar 

  37. Ghodrati M, Khaligh-Razavi S, Ebrahimpour R, Rajaei K, Pooyan M (2012) How can selection of biologically inspired features improve the performance of a robust object recognition model? Plos ONE 7(2):1–15

    Article  Google Scholar 

  38. Mutch J, Lowe DG (2008) Object class recognition and localization using sparse features with limited receptive fields. Int J Comput Vis 80(1):45–57

    Article  Google Scholar 

  39. Pshikhopov VKh, Medvedev MY, Sirotenko MY, Kostjukov VA (2009) Control system design for robotic airship. 9th IFAC symposium on robot control. Gifu, Japon, vol 42, no 16, pp 26–31

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by CONACyT through Project 155045—“Evolución de Cerebros Artificiales en Visión por Computadora” and by CICESE project number 634119. Dr. Olague graciously acknowledges the support of the Seventh Framework Programme of the European Union through the Marie Curie International Research Staff Scheme, FP7-PEOPLE-2013-IRSES, Grant 612689 ACoBSEC, project Analysis and Classification of Mental States of Vigilance with Evolutionary Computation.

Author information

Authors and Affiliations

  1. TecNM - Instituto Tecnológico de Tijuana, Calzada Del Tecnológico S/N, Fraccionamiento Tomas Aquino, C.P. 22414, Tijuana, B.C., Mexico

    Daniel E. Hernández

  2. Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, C.P. 22860, Ensenada, B.C., Mexico

    Gustavo Olague

  3. Instituto de Astronomía, Universidad Nacional Autónoma de México, Km. 103 Carretera Tijuana-Ensenada, Ensenada, B.C., Mexico

    Benjamín Hernández

  4. TecNM Instituto Tecnológico de Ensenada, Blvd. Tecnológico No. 150, Ex Ejido Chapultepec, C.P.22750, Ensenada, B.C., Mexico

    Eddie Clemente

Authors
  1. Daniel E. Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gustavo Olague
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benjamín Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eddie Clemente
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gustavo Olague.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hernández, D.E., Olague, G., Hernández, B. et al. CUDA-based parallelization of a bio-inspired model for fast object classification. Neural Comput & Applic 30, 3007–3018 (2018). https://doi.org/10.1007/s00521-017-2873-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-2873-3

Keywords

Search

Navigation