Skip to main content

Advertisement

Springer Nature Link
Log in

V4 Neural Network Model for Shape-Based Feature Extraction and Object Discrimination

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Visual area V4 plays an important role in the neural mechanism of shape recognition. V4 neurons exhibit selectivity for the orientation and curvature of boundary fragments. In this paper, we propose a novel neural network model of V4 for shape-based feature extraction and other vision tasks. The low-level layers of the model consist of computational units simulating simple cells and complex cells in the primary visual cortex. These layers extract preliminary visual features including edges and orientations. The V4 computational units calculate the entropy of the extracted features as a measure of visual saliency. The features around salient points are then selected and encoded with a layer of restricted Boltzmann machine to generate an intermediate representation of object shapes. The model is evaluated in shape distinction, feature detection, feature matching, and object discrimination experiments. The results demonstrate that this model generates discriminative local representation of object shapes. It shows a successful attempt to construct a computation model of visual object recognition in the brain.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell. 2000;22(12):1349–80.

    Article  Google Scholar 

  2. Ettlinger G. “Object vision” and “spatial vision”: the neuropsychological evidence for the distinction. Cortex. 1990;26(3):319–41.

    Article  CAS  PubMed  Google Scholar 

  3. Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol. 1962;160(1):106.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Bell AH, Hadj-Bouziane F, Frihauf JB, Tootell RBH, Ungerleider LG. Object representations in the temporal cortex of monkeys and humans as revealed by functional magnetic resonance imaging. J Neurophysiol. 2009;101(2):688–700.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Gallant JL, Connor CE, Rakshit S, Lewis JW, Van Essen DC. Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. J Neurophysiol. 1996;76(4):2718–39.

    CAS  PubMed  Google Scholar 

  6. Pasupathy A, Connor CE. Shape representation in area V4: position-specific tuning for boundary conformation. J Neurophysiol. 2001;86(5):2505–19.

    CAS  PubMed  Google Scholar 

  7. David SV, Hayden BY, Gallant JL. Spectral receptive field properties explain shape selectivity in area V4. J Neurophysiol. 2006;96(6):3492–505.

    Article  PubMed  Google Scholar 

  8. Cadieu C, Kouh M, Pasupathy A, Connor CE, Riesenhuber M, Poggio T. A model of V4 shape selectivity and invariance. J Neurophysiol. 2007;98(3):1733–50.

    Article  PubMed  Google Scholar 

  9. Riesenhuber M, Poggio T. Hierarchical models of object recognition in cortex. Nat Neurosci. 1999;2(11):1019–25.

    Article  CAS  PubMed  Google Scholar 

  10. Bengio Y. Learning deep architectures for AI. Found Trends Mach Learn. 2009;2(1):1–127.

    Article  Google Scholar 

  11. Krizhevsky A, Sutskever Il, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012. p. 1097–105.

  12. Boureau Y-L, Ponce J, LeCun Y. A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the international conference on machine learning. 2010. p. 111–8.

  13. Desimone R, Duncan J. Neural mechanisms of selective visual attention. Annu Rev Neurosci. 1995;18(1):193–222.

    Article  CAS  PubMed  Google Scholar 

  14. Sasaki Y, Vanduffel W, Knutsen T, Tyler C, Tootell R. Symmetry activates extrastriate visual cortex in human and nonhuman primates. Proc Natl Acad Sci USA. 2005;102(8):3159–63.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Hinton G. A practical guide to training restricted Boltzmann machines. Momentum. 2010;9(1):926.

    Google Scholar 

  16. Pasupathy A, Connor CE. Responses to contour features in macaque area V4. J Neurophysiol. 1999;82(5):2490–502.

    CAS  PubMed  Google Scholar 

  17. Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell. 2005;27(10):1615–30.

    Article  PubMed  Google Scholar 

  18. Lowe DG. Object recognition from local scale-invariant features. In: Proceedings of the IEEE international conference on computer vision, volume 2. IEEE, 1999. p. 1150–57.

  19. Vedaldi A, Fulkerson B. Vlfeat: an open and portable library of computer vision algorithms. In: Proceedings of the international conference on multimedia. ACM, 2010. p. 1469–72.

  20. Fei-Fei L, Fergus R, Perona P. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput Vis Image Underst. 2007;106(1):59–70.

    Article  Google Scholar 

  21. Grauman K, Darrell T. The pyramid match kernel: discriminative classification with sets of image features. In: Proceedings of the IEEE international conference on computer vision, volume 2. IEEE, 2005. p. 1458–65.

  22. Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE conference on computer vision and pattern recognition, volume 2. IEEE, 2006. p. 2169–78.

  23. Zhang H, Berg AC, Maire M, Malik J. SVM-KNN: discriminative nearest neighbor classification for visual category recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, volume 2. IEEE, 2006. p. 2126–36.

  24. Wang G, Zhang Y, Fei-Fei L. Using dependent regions for object categorization in a generative framework. In: Proceedings of the IEEE conference on computer vision and pattern recognition, volume 2. IEEE, 2006. p. 1597–604.

  25. Bosch A, Zisserman A, Munoz X. Image classification using random forests and ferns. In: Proceedings of the IEEE international conference on computer vision. IEEE, 2007. p. 1–8.

  26. Boiman O, Shechtman E, Irani M. In defense of nearest-neighbor based image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, 2008. p. 1–8.

  27. Liu B-D, Wang Y-X, Zhang Y-J, Shen B. Learning dictionary on manifolds for image classification. Pattern Recogn. 2013;46(7):1879–90.

    Article  Google Scholar 

  28. Heo B, Jeong H, Kim J, Choi S-Il, Choi JY. Weighted pooling based on visual saliency for image classification. In: Advances in visual computing. Springer, 2014. p. 647–657.

  29. Wei H, Li H. Shape description and recognition method inspired by the primary visual cortex. Cogn Comput. 2014;6(2):164–74.

    Article  Google Scholar 

  30. Wei H, Li Q, Dong Z. Learning and representing object shape through an array of orientation columns. IEEE Trans Neural Netw Learn Syst. 2014;25(7):1346–58.

    Article  Google Scholar 

  31. Zhao J, Du C, Sun H, Liu X, Sun J. Biologically motivated model for outdoor scene classification. Cogn Comput. 2013;7(1):20–33.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the NSFC project (Project No. 61375122), and the National Twelfth Five-Year Plan for Science and Technology (Project No. 2012BAI37B06). And this work was also supported (in part) by Shanghai Science and Technology Development Funds (13dz2260200, 13511504300).

Author information

Authors and Affiliations

  1. Laboratory of Cognitive Modeling and Algorithms, Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

    Hui Wei & Zheng Dong

Authors
  1. Hui Wei
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zheng Dong
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hui Wei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, H., Dong, Z. V4 Neural Network Model for Shape-Based Feature Extraction and Object Discrimination. Cogn Comput 7, 753–762 (2015). https://doi.org/10.1007/s12559-015-9361-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-015-9361-9

Keywords