Object Recognition Using Hierarchical Temporal Memory
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Abstract
At this time, great effort is being directed toward developing problem-solving technology that mimic human cognitive processes. Research has been done to develop object recognition using Computer Vision for daily tasks such as secure access, traffic management, and robotic behavior. For this research, four different machine learning algorithms have been developed to overcome the computer vision problem of object recognition. Hierarchical temporal memory (HTM) is an emerging technology based on biological methods of the human cortex to learn patterns. This research applied an HTM algorithm to images (video sequences) in order to compare this technique against two others: support vector machines (SVM) and artificial neural networks (ANN). It was concluded that HTM was the most effective.
Keywords
Machine learning Computer vision HTMNotes
Acknowledgements
We would like to thank our colleagues from the Happy Few Research Group for their support during the development of this research. In addition, we thank the PARMA Group for its guidance and support publishing this research.
References
- 1.Jalal, A.S., Singh, V.: Visual object tracking: state of art. Int. J. Comput. Inform. 3, 227–247 (2011). Ljubljana, SloveniaGoogle Scholar
- 2.Hawkins, J., Blakeslee, S.: On Intelligence. St. Martin’s Griffin, USA (2005)Google Scholar
- 3.Fan, J., Xu, W., Gong, Y.: Convolutional neural networks: human traffic. IEEE Trans. About Neural Netw., 1610–1623 (2010). https://doi.org/10.1109/TNN.2010.2066286
- 4.Hawkins, J., Ahmad, S., Dubinsky, D.: Cortical Learning Algorithms and HTM. Numenta Inc., California (2011)Google Scholar
- 5.Metz, C.E.: Principles of ROC Analysis. University of Chicago and the Franklin McLean Memorial Research Institute, Chicago, USA, pp. 283–298 (1978). 0001-2998/78/0804-0003S02.00/0Google Scholar
- 6.Lowe, D.G.: Object recognition based on local scale invariant features. In: Computer Vision: International Conference, pp. 1150–1157. IEEE, Canada (1999). https://doi.org/10.1109/ICCV.1999.790410
- 7.Solem, J.E.: Python: Basics on Computer Vision. O’Reilly Medias, Sebastopol (2012)Google Scholar
- 8.Hassner, T., Mayzels, V., Zelnik-Manor, L.: About SIFT and its scale. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Rhode Island, USA (2012)Google Scholar
- 9.Han, B., Li, D., Ji, J.: DSIFT Algorithm for People Detection. Stanford University, California (2011)Google Scholar
- 10.Costa, A.: A MNIST Classifier Using OPF (2016). http://github.com/allanino/nupic-classifier-mnist
- 11.Altman, N.S.: Introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46, 175–185 (1992)MathSciNetGoogle Scholar
- 12.Schaul, T., Bayer, J., Wierstra, D., Sun, Y., Felder, M., Sehnke, F., Rückstieß, T., Schmidhuber, J.: The PyBrain library. J. Mach. Learn. Res. 11, 743–746 (2011)Google Scholar
- 13.Bishop, C.M.: Pattern Recognition Using Neural Networks. Oxford University Press, Oxford (1995)Google Scholar
- 14.Blum, A.: Neural Networks (C++). Wiley, New York (1992)Google Scholar
- 15.Numenta: Nupic: managing vision tasks (2017). http://github.com/numenta
- 16.Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2017). http://www.csie.ntu.edu.tw/cjlin/libsvm/
- 17.Ihaka, R.: History of R: Past and Future. The University of Auckland, Auckland (1998)Google Scholar
- 18.Ferré, J., Rius, X.: Introduction to the Statistical Design of Experiments. Universitat Rovira i Virgili, Tarragona (2001)Google Scholar
- 19.Anderson, M., Whitcomb, P.: Design of Experiments: A Simplified Approach. Taylor and Francis Group, Boca Raton (2007)Google Scholar
- 20.Massart, D.L., Smeyers-Verbeke, J., Capron, X., Schlesier, K.: Means of Box Plots: Visual Presentation of Data. Vrije Universiteit Brussel, Brussel, Belgium (2005)Google Scholar
- 21.Schlag, I.: On Hierarchical Temporal Memory (2016). http://ischlag.github.io/2016/04/25/on-hierarchical-temporal-memory
- 22.Gerstner, W.: Hebbian learning and plasticity. From Neuron to Cognition via Computational Neuroscience, Chap. 9. MIT Press, Cambridge (2011)Google Scholar