A Computational Visual Neuroscience Model for Object Recognition

Document Type : Original Articles

Authors

Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Abstract

In this study with the inspirations from both neuroscience and computer science, a combinatorial framework for object recognition was proposed having benefited from the advantages of both biologically-inspired HMAX_S architecture model for feature extraction and Extreme Learning Machine (ELM) as a classifier. HMAX model is a feed-forward hierarchical structure resembling the ventral pathway in the visual cortex of the brain and ELM is a powerful neural network, which randomly chooses hidden nodes and specifies analytically the single-hidden layer. ELM theories conjecture that this randomness may be true for biological learning in animal brains. It should be noted that the principle reason of using ELM is mainly as a result of its biological structure in order to imitate the biological object recognition system of mammalians and partly for its incredible speed which drastically lessens the runtime. Classification results are reported in Caltech101 dataset, at the focal point with its combinatorial framework serving considerable improvements over latest studies in both classification rate (96.39%) and the low runtime (0.417s).

Keywords

References

  1. Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology. 1962; 160(1):106-54. doi: 10.1113/jphysiol.1962.sp006837
  2. Riesenhuber M, Poggio T. Hierarchical models of object recognition in cortex. Nature Neuroscience. 1999; 2(11):1019–25. doi: 10.1038/14819
  3. 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. 2007; 29(3):411–26. doi: 10.1109/tpami.2007.56
  4. Mutch J, Lowe DG. Object class recognition and localization using sparse features with limited receptive fields. International Journal of Computer Vision. 2008; 80(1):45–57. doi: 10.1007/s11263-007-0118-0
  5. Lee YJ, Tsai CY, Chen LG. A cortex-like model for rapid object recognition using feature-selective hashing. Paper presented at: The 2011 International Joint Conference; 2011 31 Jul - 5 Aug; San Jose, CA, USA.
  6. Seifzadeh S, Faez K. A cortex-like model for animal recognition based on texture using feature-selective hashing. Paper presented at: The 2014 Iranian Conference on Intelligent Systems (ICIS); 2014 Feb 4-6; Bam, Iran.
  7. Theriault C, Thome N, Cord M. Extended Coding and Pooling in the HMAX Model. IEEE Transactions on Image Processing. 2013; 22(2):764–77. doi: 10.1109/tip.2012.2222900
  8. Huang GB. An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation. 2014; 6(3):376–90. doi: 10.1007/s12559-014-9255-2
  9. Lee H, Grosse R, Ranganath R, Ng AY. Convolutional deep belief networks for scalable unsupervised learning of hier
Journal of Advanced Medical Sciences and Applied Technologies
Volume 2, Issue 4 - Serial Number 4
9
December 2016
Pages 313-320

Files

Share

How to cite

Statistics