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The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks Cover

The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks

Journal of Artificial Intelligence and Soft Computing Research
Volume 4 (2014): Issue 1 (January 2014)
By: Joseph Lin Chu and  Adam Krzyźak  
Open Access
|Dec 2014

References

  1. [1] D. H. Ackley, G. E. Hinton, and T. J. Sejnowski. A learning algorithm for boltzmann machines. Cognitive Science, 9:147-169, 1985.10.1207/s15516709cog0901_7
    Search in Google Scholar
  2. [2] Y. Bengio. Learning deep architectures for ai. Foundations and Trends in Machine Learning, 2(1):1-127, 2009.10.1561/2200000006
    Search in Google Scholar
  3. [3] Chih-Chung Chang and Chih-Jen Lin. 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.10.1145/1961189.1961199
    Search in Google Scholar
  4. [4] Joseph Lin Chu and Adam Krzy˙zak. Application of support vector machines, convolutional neural networks and deep belief networks to recognition of partially occluded objects. In L. Rutkowski, editor, The 13th International Conference on Artificial Intelligence and Soft Computing ICAISC 2014, Lecture Notes on Artifical Intelligece (LNAI), volume 8467, pages 34-46. Springer International Publishing Switzerland, 2014.10.1007/978-3-319-07173-2_4
    Search in Google Scholar
  5. [5] R. Collobert and S. Bengio. Links between perceptrons, mlps and svms. Proceedings of the 21st International Conference on Machine Learning, page 23, 2004.10.1145/1015330.1015415
    Search in Google Scholar
  6. [6] C. Cortes and V. N. Vapnik. Support-vector networks. Machine Learning, 20:273-297, 1995.10.1007/BF00994018
    Search in Google Scholar
  7. [7] K. Fukushima. Neocognitron for handwritten digit recognition. Neurocomputing, 51:161-180, 2003.10.1016/S0925-2312(02)00614-8
    Search in Google Scholar
  8. [8] Kunihiko Fukushima and Sei Miyake. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition, 15(6):455-469, 1982.
    Search in Google Scholar
  9. [9] G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800, 2002.10.1162/08997660276012801812180402
    Search in Google Scholar
  10. [10] G. E. Hinton. A practical guide to training restricted boltzmann machines. Momentum, 9(1):599-619, 2010.10.1007/978-3-642-35289-8_32
    Search in Google Scholar
  11. [11] G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554, 2006.10.1162/neco.2006.18.7.152716764513
    Search in Google Scholar
  12. [12] G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313:504-507, 2006.10.1126/science.112764716873662
    Search in Google Scholar
  13. [13] J. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the USA, 79(8):2554-2558, 1982.10.1073/pnas.79.8.25543462386953413
    Search in Google Scholar
  14. [14] F. J. Huang and Y. LeCun. Large-scale learning with svm and convolutional nets for generic object categorization. Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1:284-291, 2006.
    Search in Google Scholar
  15. [15] D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in a cat’s visual cortex. Journal of Physiology (London), 160:106-154, 1962.10.1113/jphysiol.1962.sp006837135952314449617
    Search in Google Scholar
  16. [16] Y. LeCun, L. Bottou, Y. Bengio, and Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.10.1109/5.726791
    Search in Google Scholar
  17. [17] Y. LeCun, F.J. Huang, and L. Bottou. Learning methods for generic object recognition with invariance to pose and lighting. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2:97-104, 2004.
    Search in Google Scholar
  18. [18] Aleix M. Mart´ınez. Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6):748-763, 2002.10.1109/TPAMI.2002.1008382
    Search in Google Scholar
  19. [19] V. Nair and G. E. Hinton. 3d object recognition with deep belief nets. Advances in Neural Information Processing Systems (NIPS), pages 1339-1347, 2009.
    Search in Google Scholar
  20. [20] M. Ranzato, J. Susskind, V. Mnih, and G. Hinton. On deep generative models with applications to recognition. 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2857-2864, 2011.
    Search in Google Scholar
  21. [21] M. A. Ranzato, F. J. Huang, Y. L. Boureau, and Y. LeCun. Unsupervised learning of invariant feature hierarchies with applications to object recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, 2007.
    Search in Google Scholar
  22. [22] P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. In David E. Rumelhart and James L. McLelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1, chapter 6, pages 194-281. MIT Press, 1986.
    Search in Google Scholar
  23. [23] P.W. M. Tsang and P. C. Yuen. Recognition of partially occluded objects. IEEE Transactions on Systems, Man and Cybernetics, 23(1):228-236, 1993.10.1109/21.214781
    Search in Google Scholar
  24. [24] John Winn and Jamie Shotton. The layout consistent random field for recognizing and segmenting partially occluded objects. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1:37-44, 2006.
    Search in Google Scholar
  25. [25] LaurenzWiskott and Christoph Von Der Malsburg. A neural system for the recognition of partially occluded objects in cluttered scenes: A pilot study. International Journal of Pattern Recognition and Artificial Intelligence, 7(4):935-948, 1993
    Search in Google Scholar
DOI: https://doi.org/10.2478/jaiscr-2014-0021
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Language: English
Page range: 5 - 19
Published on: Dec 30, 2014
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
Computer sciences,
Artificial intelligence,
Databases and data mining

© 2014 Joseph Lin Chu, Adam Krzyźak, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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