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An enhanced HMAX model in combination with SIFT algorithm for object recognition

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Abstract

Hierarchical model and X (HMAX), which is a feedforward network, has displayed profoundly satisfying performance for object recognition tasks in comparison with other state-of-the-art machine vision algorithms. Nevertheless, the standard HMAX model has two major drawbacks. The first one is the computational cost of the S2 layer. The second one is random patch selection of HMAX model, which leads to low performance as meaningless and redundant patches are extracted. In this paper, a faster and more accurate HMAX model in combination with scale-invariant feature transform algorithm is proposed to improve mentioned weaknesses. Our proposed model consists of two levels of improvement. The first level is increasing the speed of matching in S2 layer by comparing the extracted patches with only a few informative patches rather than the whole image. The second one is related to the performance improvement by extracting the discriminative and distinctive patches in the training stage. The obtained results prove that the proposed model performs classification tasks faster than both the standard HMAX model and the binary-based HMAX model (B-HMAX). Meanwhile, the performance for the proposed model stays almost as high as that of the B-HMAX model.

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References

  1. Gevers, T., Smeulders, A.W.M.: Color-based object recognition. Pattern Recognit. 32(3), 453–464 (1999)

    Article  Google Scholar 

  2. Satpathy, A., Jiang, X., Eng, H.L.: LBP-based edge-texture features for object recognition. IEEE Trans. Image Process. 23(5), 1953–1964 (2014)

    Article  MathSciNet  Google Scholar 

  3. Saraswat, M., Goswami, A.K., Tiwari, A.: Object recognition using texture based analysis. IJCSIT 4(6), 775–782 (2013)

    Google Scholar 

  4. Zhu, J., et al.: Colour combination attention for object recognition. IET Image Process. 8, 539–547 (2014)

    Article  Google Scholar 

  5. Wang, J., et al.: Texture classification using multi-resolution global and local Gabor features in pyramid space. Signal Image Video Process. 13(1), 163–170 (2019)

    Article  Google Scholar 

  6. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  7. Laptev, I.: Improving object detection with boosted histograms. Image Vis. Comput. 27(5), 535–544 (2009)

    Article  Google Scholar 

  8. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2005)

  9. Tan, H., Yang, B., Ma, Z.: Face recognition based on the fusion of global and local HOG features of face images. IET Comput. Vis. 8(3), 224–234 (2014)

    Article  Google Scholar 

  10. Murtaza, F., Yousaf, M.H., Velastin, S.A.: Multi-view human action recognition using 2D motion templates based on MHIs and their HOG description. IET Comput. Vis. 10(7), 758–767 (2016)

    Article  Google Scholar 

  11. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  12. Bay, H., et al.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  13. Lu, X., et al.: Locally aggregated histogram-based descriptors. Signal Image Video Process. 12(2), 323–330 (2018)

    Article  Google Scholar 

  14. Han, H., et al.: Hierarchical spatial pyramid max pooling based on SIFT features and sparse coding for image classification. IET Comput. Vis. 7(2), 144–150 (2013)

    Article  Google Scholar 

  15. Ledoux, A., Richard, N.: Color and multiscale texture features from vectorial mathematical morphology. Signal Image Video Process. 10(3), 431–438 (2016)

    Article  Google Scholar 

  16. Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1959)

    Article  Google Scholar 

  17. Campbell, F.W., Fau-Robson, J.G., Robson, J.G.: Application of Fourier analysis to the visibility of gratings. J. Physiol. 197(3), 551 (1968)

    Article  Google Scholar 

  18. Henning, G.F., Hertz, B.G., Broadbent, D.E.: Some experiments bearing on the hypothesis that the visual system analyses spatial patterns in independent bands of spatial frequency. Vision. Res. 15(8–9), 887–897 (1975)

    Article  Google Scholar 

  19. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019–1025 (1999)

    Article  Google Scholar 

  20. Serre, T., et al.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)

    Article  Google Scholar 

  21. Theriault, C., Thome, N., Cord, M.: Extended coding and pooling in the HMAX model. IEEE Trans. Image Process. 22(2), 764–777 (2013)

    Article  MathSciNet  Google Scholar 

  22. Lu, Y.F., et al.: Enhanced hierarchical model of object recognition based on a novel patch selection method in salient regions. IET Comput. Vis. 9(5), 663–672 (2015)

    Article  Google Scholar 

  23. Zhang, Y., Zhang, L., Li, P.: A novel biologically inspired ELM-based network for image recognition. Neurocomputing 174(Part A), 286–298 (2016)

    Article  Google Scholar 

  24. Fu, R. et al.: Accelerated HMAX model based on improved SIFT feature points. In: 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) (2015)

  25. Deng, L., et al.: Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds. Mach. Vis. Appl. 29(6), 965–977 (2018)

    Article  Google Scholar 

  26. Filali, J., Zghal, H., Martinet, J.: Ontology and HMAX features-based image classification using merged classifiers. In: International Conference on Computer Vision Theory and Applications 2019 (VISAPP’19) (2019)

  27. Zhang, H.-Z., et al.: B-HMAX: a fast binary biologically inspired model for object recognition. Neurocomputing 218, 242–250 (2016)

    Article  Google Scholar 

  28. Borji, A., et al.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)

    Article  MathSciNet  Google Scholar 

  29. Huang, Y., et al.: Enhanced biologically inspired model for object recognition. IEEE Trans. Syst. Man Cybern. 41(6), 1668–1680 (2011)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran, 16846-13114, Iran

    Mohammad Norizadeh Cherloo, Milad Shiri & Mohammad Reza Daliri

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  1. Mohammad Norizadeh Cherloo
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  2. Milad Shiri
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  3. Mohammad Reza Daliri
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Correspondence to Mohammad Reza Daliri.

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Norizadeh Cherloo, M., Shiri, M. & Daliri, M.R. An enhanced HMAX model in combination with SIFT algorithm for object recognition. SIViP 14, 425–433 (2020). https://doi.org/10.1007/s11760-019-01572-8

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  • DOI: https://doi.org/10.1007/s11760-019-01572-8

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