Feature Extraction for Object Recognition Inspired by Human Visual System

Sufikarimi, Hiwa; Mohammadi, Karim

doi:10.22041/ijbme.2018.85614.1343

Document Type : Full Research Paper

Authors

Hiwa Sufikarimi ¹
Karim Mohammadi ²

¹ Ph.D Student, Electronic School, Electrical Engineering Faculty, Iran University of Science and Technology, Tehran, Iran

² Professor, Electronic School, Electrical Engineering Faculty, Iran University of Science and Technology, Tehran, Iran

https://doi.org/10.22041/ijbme.2018.85614.1343

Abstract

In this paper, we tried to present a robust and reliable approach to object recognition by inspiring human visual system. A famous model, inspiring mammalian visual system, is HMAX (Hierarchical Model and X). It shows significant accuracy rates on object recognition tasks. However, there are some differences between this model and human visual system. Indeed cortex's functions are not properly modeled. Unrepeatability under fixed conditions, redundancy, high computing load and being slow are some drawbacks of HMAX. By modeling the secondary visual cortex and adding to the HMAX, we tried to introduce a more accurate model of the human visual system and cover the drawbacks of the previous models. The proposed approach functionally mimics the secondary visual cortex. Attending to high-level features, selecting discriminative and repeatable features, it has higher performance than standard HMAX. The added parts have negligible computation load. Therefore, it does not slow down this model. On the contrary, by selecting brief and useful features, the speed of the model is increased. The proposed approach is compared to the standard HMAX in terms of speed and accuracy rate. The results showed the advantage of proposed approach rather than the standard HMAX. In addition, the effect of the number of features and training images on their performance was shown. It is shown that the proposed approach has a better performance than the standard HMAX especially when the number of feature and training images is small.

Keywords

Main Subjects

Biomimetics

References

[1] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Comput. Vis. Image Underst., vol. 110, no. 3, pp. 346–359, Jun. 2008.

[2] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.

[3] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, vol. 1, pp. 886–893.

[4] P. Moreno, M. J. Marín-Jiménez, A. Bernardino, J. Santos-Victor, and N. P. de la Blanca, “A Comparative Study of Local Descriptors for Object Category Recognition: SIFT vs HMAX,” in Pattern Recognition and Image Analysis, no. June, 2007, pp. 515–522.

[5] P. T. Riesenhuber M, “Hierarchical models of object recognition in cortex,” Nat. Neurosci., pp. 1019–1025, 1999.

[6] T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, “Robust object recognition with cortex-like mechanisms. TL - 29,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29 VN-r, no. 3, pp. 411–426, 2007.

[7] S. Seifzadeh, M. Rezaei, and O. Farahbakhsh, “A Computational Visual Neuroscience Model for Object Recognition,” J. Adv. Med. Sci. Appl. Technol., vol. 2, no. 4, p. 315, Jan. 2017.

[8] H.-Z. Zhang, Y.-F. Lu, T.-K. Kang, and M.-T. Lim, “B-HMAX: A fast binary biologically inspired model for object recognition,” Neurocomputing, vol. 218, pp. 242–250, Dec. 2016.

[9] Yulong Wang, Qingtian Zhang, and Xiaolin Hu, “Distributed sparse HMAX model,” in 2015 Chinese Automation Congress (CAC), 2015, no. 1, pp. 740–745.

[10] Y. Li, W. Wu, B. Zhang, and F. Li, “Enhanced HMAX model with feedforward feature learning for multiclass categorization,” Front. Comput. Neurosci., vol. 9, no. October, pp. 1–14, Oct. 2015.

[11] C. Theriault, N. Thome, and M. Cord, “Extended Coding and Pooling in the HMAX Model,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 764–777, Feb. 2013.

[12] P. Mishra and B. K. Jenkins, “Hierarchical model for object recognition based on natural-stimuli adapted filters,” in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, pp. 950–953.

[13] D. B. Walther and C. Koch, “Attention in hierarchical models of object recognition,” in Progress in Brain Research, vol. 165, no. 06, 2007, pp. 57–78.

[14] H. Sufikarimi and K. Mohammadi, “Speed up biological inspired object recognition, HMAX,” in 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), 2017, pp. 183–187.

[15] M. Jazlaeiyan and H. S. Shahhoseini, “Optimal Feature Selection in Biologically Inspired Model for Object Recognition Using Mutual Information Maximisation,” Iran. J. Biomed. Eng., vol. 8, no. 4, pp. 371–383, 2015.

[16] M. Ghodrati, S.-M. Khaligh-Razavi, R. Ebrahimpour, K. Rajaei, and M. Pooyan, “How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?,” PLoS One, vol. 7, no. 2, p. e32357, Feb. 2012.

[17] J. Mutch and D. G. Lowe, “Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields,” Int. J. Comput. Vis., vol. 80, no. 1, pp. 45–57, Oct. 2008.

[18] Y. Lu, M. Lim, H. Zhang, and T. Kang, “Enhanced hierarchical model of object recognition based on a novel patch selection method in salient regions,” IET Comput. Vis., vol. 9, no. 5, pp. 663–672, Oct. 2015.

[19] I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding,” Psychol. Rev., vol. 94, no. 2, pp. 115–147, 1987.

[20] A. Al Maashri, M. DeBole, C.-L. Yu, V. Narayanan, and C. Chakrabarti, “A hardware architecture for accelerating neuromorphic vision algorithms,” in 2011 IEEE Workshop on Signal Processing Systems (SiPS), 2011, pp. 355–360.

[21] Z. Guo and Z. J. Wang, “An Unsupervised Hierarchical Feature Learning Framework for One-Shot Image Recognition,” IEEE Trans. Multimed., vol. 15, no. 3, pp. 621–632, Apr. 2013.

[22] B. Yang, L. Zhou, and Z. Deng, “C-HMAX: Artificial cognitive model inspired by the color vision mechanism of the human brain,” Tsinghua Sci. Technol., vol. 18, no. 1, pp. 51–56, 2013.

[23] T. Serre, L. Wolf, and T. Poggio, “Object Recognition with Features Inspired by Visual Cortex,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2007, vol. 2, pp. 994–1000.

[24] K. D. Flemming, “Essential Neuroscience,” Mayo Clin. Proc., vol. 81, no. 10, p. 1409, Oct. 2006.

Iranian Journal of Biomedical Engineering

Feature Extraction for Object Recognition Inspired by Human Visual System

References

References

Volume 11, Issue 4
February 2018
Pages 337-349

Feature Extraction for Object Recognition Inspired by Human Visual System

References

References

Volume 11, Issue 4February 2018Pages 337-349

Volume 11, Issue 4
February 2018
Pages 337-349