Rotation-Invariant Image Description from Independent Component Analysis for Classification Purposes

Rodrigo D. C. da Silva, George A. P. Thé, Fátima N. S. de Medeiros


Independent component analysis (ICA) is a recent technique used in signal processing for feature description in classification systems, as well as in signal separation, with applications ranging from computer vision to economics. In this paper we propose a preprocessing step in order to make ICA algorithm efficient for rotation invariant feature description of images. Tests were carried out on five datasets and the extracted descriptors were used as inputs to the k-nearest neighbor (k-NN) classifier. Results showed an increasing trend on the recognition rate, which approached 100%. Additionally, when low-resolution images acquired from an industrial time-of-flight sensor are used, the recognition rate increased up to 93.33%.


  1. Ali, A., Gilani, A. M., Memon, N. A., 2006. Affine Normalized Invariant functionals using Independent Component Analysis. Multitopic Conference. INMIC 7806. IEEE, pp. 94-99, 23-24 December.
  2. Bach, F. R., Jordan, M. I., 2002. Kernel Independent Component Analysis, Journal of Machine Learning Research, vol. 3, pp. 1-48.
  3. Bell, A. J., Sejnowski, T. J., 1995. An InformationMaximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol. 7, pp. 1129- 1159.
  4. Bizon, K., Lombardi, S., Continillo, G., Mancaruso, E., Vaglieco, B. M., 2013. Analysis of Diesel Engine Combustion using Imaging and Independent Component Analysis. Proceedings of the Combustion Institute, vol. 34, pp. 2921-2931.
  5. Cardoso, J. F., 1989. Source Separation using Higher Order Moments, ICASSP, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 2109-2112.
  6. Chattopadhyay, A. K., Mondal, S., Chattopadhyay, T., 2013. Independent Component Analysis for the Objective Classification of Globular Clusters of the Galaxy NGC 5128, Computational Statistics & Data Analysis, vol. 57, issue 1, pp. 17-32.
  7. Cichy, R., Pantazis, D., Oliva, A., 2013, “Mapping Visual Object Recognition in the Human Brain with Combined MEG and fMRI”, Journal of Vision, vol. 13, n. 9, pp. 659.
  8. Coomans, D., Massart, D. L., 1981. Alternative k-Nearest Neighbor Rules in Supervised Pattern Recognition. Part. 1. k-nearest neighbor classification using alternative voting rules. Analytica Chimica Acta, vol. 136, pp. 15-27.
  9. Cover, T. M., 1968, Estimation by the Nearest Neighbor Rule, IEEE Transactions on Information Theory, vol. 14, issue 1, pp. 50-55.
  10. Déniz, O., Castrillón, M., Hernández, M., 2003. Face Recognition using Independent Component Analysis and Support Vector Machines. Pattern Recognition Letters, vol. 24, issue 13, pp. 2153-2157.
  11. Fan, L., Long, F., Zhang, D., Guo, X., Wu, X., 2002. Applications of independent component analysis to image feature extraction, Second International Conference on Image and Graphics, vol. 4875, pp. 471-476.
  12. Hastie, T., Tibshirani, R., 2003. In: Becker, S., Obermayer, K. (Eds.), Independent Component Analysis through Product Density Estimation in Advances in Neural Information Processing System, vol. 15, MIT Press, Cambridge, MA, pp. 649-656.
  13. Huang, Z., Cohen, F. S., 1996, “Affine-invariant B-Spline moment for curve matching”, IEEE Trans. Image Process., vol. 5, n. 10, pp. 1473-1490.
  14. Huang, X., Wang, B., Zhang, L., 2005. A New Scheme for Extraction of Affine Invariant Descriptor and Affine Motion Estimation based on Independent Component Analysis, Pattern Recognition Letters, vol. 26, issue 9, pp. 1244-1255.
  15. Hyvärinen, A., Karhunen, J., Oja, E., 2001. Independent Components Analysis. John Wiley & Sons, Inc., Canada (Chapter 6-8).
  16. Hyvärinen, A., Oja, E., 2000. 'Independent Component Analysis: Algorithms and Applications', Neural Networks, vol. 13, pp. 411-430.
  17. Jutten, C. and Herault, J., “Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture”, Signal Processing, vol. 24, pp. 1-10, 1991.
  18. Khalil, M. I., Bayoumi, M. M., 2002, “Affine invariants for object recognition using the wavelet transform”, Pattern Recognition Letters, vol. 23, pp. 57-72.
  19. Khorshidi, G. S., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., Smith, S. M., 2014. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers, NeuroImage, vol. 90, pp. 449- 468.
  20. Lima, M. A. A., Cerqueira, A. S., Coury, D. V., Duque, C. A., 2012. A Novel Method for Power Quality Multiple Disturbance Decomposition based on Independent Component Analysis, International Journal of Electrical Power & Energy Systems, vol. 42, issue 1, pp. 593-604.
  21. Lin, T.-Y., Chiu, S.-H., 2013. Using Independent Component Analysis and Network DEA to Improve Bank Performance Evaluation, Economic Modelling, vol. 32, pp. 608-616.
  22. Masoum, S., Seifi, H., Ebrahimabadi, E. H., 2013. Characterization of Volatile Components in Calligonum Comosum by Coupling Gas Chromatography-Mass Spectrometry and Mean Field Approach Independent Component Analysis, Analytical Methods, vol. 5, issue 18, pp. 4639-4647.
  23. Mercimek, M., Gulez, K., Mumcu, T. V., 2005. Real Object Recognition using Moment Invariants. Sadhana, vol. 30, issue 6, pp. 765-775.
  24. Mukundan, R., Ong, S.H., Lee, P.A., 2001. Image analysis by Tchebichef moments, IEEE Trans. Image Process. vol. 10, issue 9, pp. 1357-1364.
  25. Oirrak, A. E., Daoudi, M., Aboutajdine, D., 2002. Affine Invariant Descriptors using Fourier Series. Pattern Recognition Letters. vol. 23, pp. 1109-1118.
  26. Pan, H. et al, 2013, “Efficient and Accurate Face Detection using Heterogeneous Feature Descriptors and Features Selection”, Computer Vision and Image Understanding, vol. 117, n. 1, pp. 12-28.
  27. Park, B., Kim, D-S., Park, H-J., 2014. Graph Independent Component Analysis Reveals Repertoires of Intrinsic Network Components in the Human Brain. PLoS ONE 9(1): e82873. doi:10.1371/journal.pone.0082873.
  28. Rojas, F., García, R. V., González, J., Velázquez, L., Becerra, R., Valenzuela, O., B. Román, S., 2013. Identification of Saccadic Components in Spinocerebellar Ataxia Applying an Independent Component Analysis Algorithm, Neurocomputing, vol. 121, pp. 53-63.
  29. Safia A., He D., 2013. New Brodatz-based Image Databases for Grayscale Color and Multiband Texture Analysis, ISRN Machine Vision, Article ID 876386, 1. Available at http://multibandtexture.recherche.
  30. Sanchetta, A. C. et al, 2014, “Facies Recogniton using a Smoothing Process through Fast Independent Component Analysis and Discrete Cosine Transform”, Computer & Geosciences, vol. 57, pp. 175-182.
  31. Sindhumol S., Kumar, A., Balakrishnan, K., 2013. Spectral Clustering Independent Component Analysis for Tissue Classification from Brain MRI, Biomedical Signal Processing and Control, vol. 8, issue 6, pp. 667-674.
  32. Tong, Y., Hocke, L.M., Nickerson, L.D., Licata, S.C., Lindsey, K.P., Frederick, B.D., 2013. Evaluating the Effects of Systemic Low Frequency Oscillations Measured in the Periphery on the Independent Component Analysis Results of Resting State Networks, NeuroImage, vol. 76, pp. 202-215.
  33. Yuen, P. C., Lai, J. H., 2002. Face Representation using Independent Component Analysis. Pattern Recognition, vol. 35, pp. 1247-1257.
  34. Zhao, A., Chen, J., 1997. Affine Curve Moment Invariants for Shape Recognition. Pattern Recognition. vol. 30, issue 6, pp. 895-901.

Paper Citation

in Harvard Style

da Silva R., A. P. Thé G. and de Medeiros F. (2015). Rotation-Invariant Image Description from Independent Component Analysis for Classification Purposes . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-123-6, pages 210-216. DOI: 10.5220/0005512802100216

in Bibtex Style

author={Rodrigo D. C. da Silva and George A. P. Thé and Fátima N. S. de Medeiros},
title={Rotation-Invariant Image Description from Independent Component Analysis for Classification Purposes},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},

in EndNote Style

JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Rotation-Invariant Image Description from Independent Component Analysis for Classification Purposes
SN - 978-989-758-123-6
AU - da Silva R.
AU - A. P. Thé G.
AU - de Medeiros F.
PY - 2015
SP - 210
EP - 216
DO - 10.5220/0005512802100216