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Hierarchical Object Recognition Model of Increased Invariance

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Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 383))

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Abstract

The object recognition model described in this paper enhances the performance of recent pioneering attempts that simulate the primary visual cortex operations. Images are transformed into the log-polar space in order to achieve rotation invariance, resembling the receptive fields (RF) of retinal cells. Via the L*a*b colour-opponent space and log-Gabor filters, colour and shape features are processed in a manner similar to V1 cortical cells. Visual attention is employed to isolate an object’s regions of interest (ROI) and through hierarchicallayers visual information is reduced to vector sequences learned by a classifier. Template matching is performed with the normalised cross-correlation coefficient and results are obtained from the frequently used Support Vector Machine (SVM) and a Spectral Regression Discriminant Analysis (SRDA) classifier. Experiments on five different datasets demonstrate that the proposed model has an improved recognition rate and robust rotation invariance with low standard deviation values across the rotation angles examined.

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References

  1. Mishkin, M., Ungerleider, G., Macko, A.K.: Object vision and spatial vision: Two cortical pathways. Trends in Neuroscience 6, 414–417 (1983)

    Article  Google Scholar 

  2. Han, S., Vasconcelos, N.: Biologically plausible saliency mechanisms improve feedforward object recognition. Vision Research 50, 2295–2307 (2010)

    Article  Google Scholar 

  3. Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)

    Article  MATH  Google Scholar 

  4. Tsitiridis, A., Yuen, P., Hong, K., Chen, T., Kam, F., Jackman, J., James, D., Richardson, M.: A biological cortex-like target recognition and tracking in cluttered background. In: SPIE, Optics and Photonics for Counterterrorism and Crime Fighting, Berlin, p. 74860G (2009)

    Google Scholar 

  5. Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. In: Advances in Neural Information Processing Systems, vol. 19 (2007)

    Google Scholar 

  6. Mutch, J., Lowe, D.: Object class recognition and localisation using sparse features with limited receptive fields. International Journal of Computer Vision 80, 45–57 (2008)

    Article  Google Scholar 

  7. Elazary, L., Itti, L.: A Bayesian model for efficient visual search and recognition. Vision Research 50, 1338–1352 (2010)

    Article  Google Scholar 

  8. Lowe, D.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 1150–1157 (1999)

    Google Scholar 

  9. Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M.: Robust Object Recognition with Cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 411–426 (2007)

    Article  Google Scholar 

  10. Tsitiridis, A., Richardson, M., Yuen, P.: Salient feature-based object recogniton in cortex-like machine vision. Engineerng Intelligent Systems. Special Is. (2012)

    Google Scholar 

  11. Engel, S., Zhang, X., Wandell, B.: Colour Tuning in Human Visual Cortex Measured with functional Magnetic Resonance Imaging. Nature 388, 68–71 (1997)

    Article  Google Scholar 

  12. Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of Optical Society of America 2, 1160–1169 (1985)

    Article  Google Scholar 

  13. Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. Journal of Optical Society of America A 4, 2379–2394 (1987)

    Article  Google Scholar 

  14. DeValois, R., Albrecht, D., Thorell, L.: Spatial Frequency Selectivity of Cells in Macaque Visual Cortex. Vision Research 22, 545–559 (1982)

    Article  Google Scholar 

  15. Fischer, S., Sroubek, F., Perrinet, L., Redondo, R., Cristobal, G.: Self-Invertible 2D Log-Gabor Wavelets. International Journal of Computer Vision 75, 231–246 (2007)

    Article  Google Scholar 

  16. Van Essen, D.C., Newsome, T.W., Maunsell, J.H.: The visual field representation in striate cortex of the macaque monkey: Asymmetries, anisotropies and individual variability. Vision Research 24, 429–448 (1984)

    Article  Google Scholar 

  17. Johnston, A.: A spatial property of the retino-cortical mapping. Spatial Vision 1, 319–331 (1986)

    Article  Google Scholar 

  18. Hollard, V.D., Delius, J.D.: Rotational Invariance in Visual Pattern Recognition. Science 218, 804–806 (1982)

    Article  Google Scholar 

  19. Harris, I.M., Dux, P.E.: Orientation-invariant object recognition: evidence from repetition blindness. Cognition 95, 73–93 (2005)

    Article  Google Scholar 

  20. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. Machine Learning Research 9, 1871–1874 (2008)

    MATH  Google Scholar 

  21. Cai, D., He, X., Han, J.: SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis. IEEE Transactions on Knowledge and Data Engineering 20, 1–12 (2008)

    Article  Google Scholar 

  22. Lazebnik, S., Schmid, C., Ponce, J.: Semi-Local Affine Parts for Object Recognition. In: Proceedings of the British Machine Vision Conference, London, pp. 959–968 (2004)

    Google Scholar 

  23. Lazebnik, S., Schmid, C., Ponce, J.: A Sparse Texture Representation Using Local Affine Regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1265–1278 (2005)

    Article  Google Scholar 

  24. Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200 (2010)

    Google Scholar 

  25. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: Computer Vision and Pattern Recognition (2009)

    Google Scholar 

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Tsitiridis, A., Mora, B., Richardson, M. (2013). Hierarchical Object Recognition Model of Increased Invariance. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-41013-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41012-3

  • Online ISBN: 978-3-642-41013-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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