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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Using Spatial Pooler of Hierarchical Temporal Memory for object classification in noisy video streams

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DOI: http://dx.doi.org/10.15439/2016F133

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 271–274 ()

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Abstract. This paper focuses on analyzing a Spatial Pooler (SP) of Hierarchical Temporal Memory (HTM) ability for facilitating object classification in noisy video streams. In particular, we seek to determine whether employing SP as a component of the video system increases overall robustness to noise. We have implemented our own version of HTM and applied it to object recognition tasks under various testing conditions. The system is composed of a video preprocessing block, a dimensionality reduction section which contains SP, a histograms collecting module and SVM classifier.

References

  1. S. Sengupta, H. Wang, W. Blackburn, and P. Ojha, “Spatial information in classification of activity videos,” in Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. Maciaszek, and M. Paprzycki, Eds., vol. 5. IEEE, oct 2015. http://dx.doi.org/10.15439/2015F382 pp. 145–153.
  2. J. F. Sowa, “The Cognitive Cycle,” in Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. Maciaszek, and M. Paprzycki, Eds., vol. 5. IEEE, oct 2015. http://dx.doi.org/10.15439/2015F003 pp. 11–16.
  3. V. Mountcastle, “The columnar organization of the neocortex,” Brain, vol. 120, no. 4, pp. 701–722, apr 1997. http://dx.doi.org/10.1093/brain/120.4.701
  4. “The Human Brain Project - Human Brain Project,” (Accessed on 10.04.2016). https://www.humanbrainproject.eu
  5. J. Hawkins, S. Ahmad, and D. Dubinsky, “Hierarchical temporal memory including HTM cortical learning algorithms,” Numenta, Inc, Tech. Rep., sep 2011. http://numenta.org/resources/HTM_CorticalLearningAlgorithms.pdf
  6. H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, sep 2009. http://dx.doi.org/10.1109/TKDE.2008.239
  7. P. Zhang, X. Zhu, and L. Guo, “Mining Data Streams with Labeled and Unlabeled Training Examples,” in 2009 Ninth IEEE International Conference on Data Mining, IEEE. Miami, USA: IEEE, dec 2009. http://dx.doi.org/10.1109/ICDM.2009.76 pp. 627–636.
  8. R. N. Hota, V. Venkoparao, and A. Rajagopal, “Shape Based Object Classification for Automated Video Surveillance with Feature Selection,” in 10th International Conference on Information Technology (ICIT 2007), IEEE. Rourkela, India: IEEE, dec 2007. http://dx.doi.org/10.1109/ICIT.2007.57 pp. 97–99.
  9. Y. Bengio, A. Courville, and P. Vincent, “Representation Learning: A Review and New Perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, aug 2013. http://dx.doi.org/10.1109/TPAMI.2013.50
  10. X. Chen, W. Wang, and W. Li, “An overview of Hierarchical Temporal Memory: A new neocortex algorithm,” in Modelling, Identification & Control (ICMIC), 2012 Proceedings of International Conference on. Wuhan, China: IEEE, 2012, pp. 1004–1010.
  11. D. Rachkovskij, “Representation and processing of structures with binary sparse distributed codes,” IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 2, pp. 261–276, 2001. http://dx.doi.org/10.1109/69.917565
  12. “Hierarchical temporal memory implementation,” (Accessed on 12.04.2016). https://bitbucket.org/maciekwielgosz/htm-hardware-architecture
  13. “HTM Test Datasets,” (Accessed on 02.07.2016). http://data.wielgosz.info
  14. “Blender project - Free and Open 3D Creation Software,” (Accessed on 12.04.2016). https://www.blender.org/
  15. Joe Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond short snippets: Deep networks for video classification,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4694–4702, jun 2015. http://dx.doi.org/10.1109/CVPR.2015.7299101
  16. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-Scale Video Classification with Convolutional Neural Networks,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, jun 2014. http://dx.doi.org/10.1109/CVPR.2014.223 pp. 1725–1732.
  17. S. Zha, F. Luisier, W. Andrews, N. Srivastava, and R. Salakhutdinov, “Exploiting Image-trained CNN Architectures for Unconstrained Video Classification,” ArXiv e-prints, mar 2015. http://arxiv.org/abs/1503.04144