Advertisement

An Object Recognition Model Using Biologically Integrative Coding with Adjustable Context

  • Jinwen Xiao
  • Hui Wei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

Abstract

Many existing models of object recognition have a hierarchical architecture. They are based on the theory of hierarchy in brain of primate and cognitive process of human. The feature is simple in low layer while complex in high layer. However, the simple feature are local without global clues in these computational models. In this paper, we propose a novel method to code orientation feature which is local feature derived from receptive field of simple cells. The integrative coding in each simple feature, utilizing the global context information such as angle between orientations, is different from other methods of coding batch-based. This coding is scale-invariance since we overlook the distance between orientations. In addition, it is a method of feature learning since the size of context can be adjusted automatically according to special recognition task. Experimental results on ETH-80 data set demonstrate the effectiveness of our model.

Keywords

hierarchical architecture feature learning sparse coding receptive field(RF) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Jhuang, H., Serre, T., Wolf, L., Poggio, T.: A biologically inspired system for action recognition. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  3. 3.
    Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25, 1106–1114 (2012)Google Scholar
  4. 4.
    Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3), 411–426 (2007)CrossRefGoogle Scholar
  5. 5.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160(1), 106 (1962)Google Scholar
  6. 6.
    Wei, H., Ren, Y., Li, B.M.: A collaborative decision-making model for orientation detection. Applied Soft Computing (2012)Google Scholar
  7. 7.
    Socher, R., Huang, E.H., Pennington, J., Ng, A.Y., Manning, C.D.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. Advances in Neural Information Processing Systems 24, 801–809 (2011)Google Scholar
  8. 8.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  11. 11.
    Tomaso, P., Thomas, S.: Models of visual cortex (2013), http://www.scholarpedia.org/article/Models_of_visual_cortex
  12. 12.
    Wei, H., Ren, Y.: An orientation detection model based on fitting from multiple local hypotheses. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part II. LNCS, vol. 7664, pp. 383–391. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1458–1465. IEEE (2005)Google Scholar
  14. 14.
    Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, II–409. IEEE (2003)Google Scholar
  15. 15.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jinwen Xiao
    • 1
  • Hui Wei
    • 1
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina

Personalised recommendations