Local Gabor Wavelet-Based Feature Extraction and Evaluation

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)

Abstract

Feature extraction is an essential step in many image processing and computer vision applications. It is quite desirable that the extracted features can effectively represent an image. Furthermore, the dominant information visually perceived by human beings should be efficiently represented by the extracted features. Over the last few decades, different algorithms are proposed to address the major issues of image representations by the efficient features. Gabor wavelet is one of the most widely used filters for image feature extraction. Existing Gabor wavelet-based feature extraction methodologies unnecessarily use both the real and the imaginary coefficients, which are subsequently processed by dimensionality reduction techniques such as PCA, LDA etc. This procedure ultimately affects the overall performance of the algorithm in terms of memory requirement and the computational complexity. To address this particular issue, we proposed a local image feature extraction method by using a Gabor wavelet. In our method, an image is divided into overlapping image blocks, and subsequently each of the image blocks are separately filtered out by Gabor wavelet. Finally, the extracted coefficients are concatenated to get the proposed local feature vector. The efficacy and effectiveness of the proposed feature extraction method is evaluated using the estimation of mean square error (MSE), peak signal-to-noise ratio (PSNR), and the correlation coefficient (CC) by reconstructing the original image using the extracted features, and compared it with the original input image. All these performance evaluation measures clearly show that real coefficients of the Gabor filter alone can effectively represent an image as compared to the methods which utilize either the imaginary coefficients or the both. The major novelty of our method lies on our claim—capability of the real coefficients of a Gabor filter for image representation.

Keywords

Image feature extraction Gabor filter Image reconstruction 

References

  1. 1.
    Lisin, D.A., Mattar, M.A., Blaschko, M.B., Benfield, M.C., Learned-miller, E.G.: Combining local and global features for object class recognition. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 47–55 (2005)Google Scholar
  2. 2.
    Shen, L., Bai, L.: A review on Gabor wavelets for face recognition. Pattern Anal. Appl. 9, 273–292 (2006)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Deng, H.-B., Jin, L.-W., Zhen, L.-X., Huang, J.-C.: A new facial expression recognition method based on local Gabor filter bank and PCA plus LDA. Int. J. Inform. Tech. 11, 86–96 (2005)Google Scholar
  4. 4.
    Dongcheng, S., Fang, C., Guangyi, D.: Facial expression recognition based on Gabor wavelet phase features. In: Proceedings of the International Conference on Image and Graphics, pp. 520–523 (2013)Google Scholar
  5. 5.
    Xie, X., Lam, K.-M.: Facial expression recognition based on shape and texture. Pattern Recogn. 42, 1003–1011 (2009)CrossRefGoogle Scholar
  6. 6.
    Zhang, J., Tan. T., Ma, L.: Invariant texture segmentation for circular Gabor filters. In: Proceedings of the IEEE International Conference on Pattern Recognition, pp. 201–204 (2002)Google Scholar
  7. 7.
    Zhang, G., Ma, Z-M.: Texture feature extraction and description using Gabor wavelet in content-based medical image retrieval. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, pp. 2–4 (2007)Google Scholar
  8. 8.
    Chen, B., Liu, Z-Q.: A novel face coding scheme based on Gabor wavelet networks and genetic algorithm. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, pp. 1489–1492 (2007)Google Scholar
  9. 9.
    Sanger, T.: Stereo disparity computation using Gabor filters. Biol. Cybern. 54, 405–418 (1988)CrossRefGoogle Scholar
  10. 10.
    Lee, T.S.: Image representation using 2D Gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (1996)CrossRefGoogle Scholar
  11. 11.
    Daugman, J.G.: Complete discrete 2D Gabor transforms by neural networks for image analysis and compression. IEEE Trans. Acoust. Speech Signal Process. 36, 1169–1179 (1988)MATHCrossRefGoogle Scholar
  12. 12.
    Bhagavathy, S., Tesic, J., Manjunath, B.S.: On the Rayleigh nature of Gabor filter outputs. In: Proceedings of the IEEE International Conference on Image Processing, pp. 745–748 (2003)Google Scholar
  13. 13.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47, 7–42 (2002)MATHCrossRefGoogle Scholar
  14. 14.
    Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 195–202 (2003)Google Scholar
  15. 15.
    Pollen, D. A., Ronner, S. F.: Visual cortical neurons as localized spatial frequency filters. IEEE Trans. Syst., Man, Cybern. 13, 907–916 (1983))Google Scholar
  16. 16.
    Navarro, R., Tabernero, A.: Gaussian wavelet transform: two alternative fast implementations for images. Multidimension. Syst. Signal Process. 2, 421–436 (1991)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Electronics and Electrical EngineeringIndian Institute of TechnologyGuwahatiIndia

Personalised recommendations