Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Texture Image Retrieval Using DTCWT-SVD and Local Binary Pattern Features

  • Jiang, Dayou (Dept. of Copyright Protection, Sangmyung University) ;
  • Kim, Jongweon (Dept. of Electronics Engineering, Sangmyung University)
  • Received : 2017.03.29
  • Accepted : 2017.09.01
  • Published : 2017.12.31
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Abstract

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

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References

  1. B. S. Manjunath and W. Y. Ma, "Texture features for browsing and retrieval of image data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, 1996. https://doi.org/10.1109/34.531803
  2. J. Uddin, R. Islam, and J. M. Kim, "Texture feature extraction techniques for fault diagnosis of induction motors," Journal of Convergence, vol. 5, no. 2, pp. 15-20. 2014.
  3. M. N. Do and M. Vetterli, "Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance," IEEE Transactions on Image Processing, vol. 11, no. 2, pp. 146-158, 2002. https://doi.org/10.1109/83.982822
  4. M. Kokare, P. K. Biswas, and B. N. Chatterji, "Texture image retrieval using new rotated complex wavelet filters," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 35, no. 6, pp. 1168-1178, 2005. https://doi.org/10.1109/TSMCB.2005.850176
  5. Y. Dong, D. Tao, X. Li, J. Ma, and J. Pu, "Texture classification and retrieval using shearlets and linear regression," IEEE Transactions on Cybernetics, vol. 45, no. 3, pp. 358-369, 2015. https://doi.org/10.1109/TCYB.2014.2326059
  6. T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996. https://doi.org/10.1016/0031-3203(95)00067-4
  7. X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1635-1650, 2010. https://doi.org/10.1109/TIP.2010.2042645
  8. Z. Guo, L. Zhang, and D. Zhang, "Rotation invariant texture classification using LBP variance (LBPV) with global matching," Pattern Recognition, vol. 43, no. 3, pp. 706-716, 2010. https://doi.org/10.1016/j.patcog.2009.08.017
  9. B. Zhang, Y. Gao, S. Zhao, and J. Liu, "Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor," IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 533-544, 2010. https://doi.org/10.1109/TIP.2009.2035882
  10. S. Murala, R. P. Maheshwari, and R. Balasubramanian, "Local tetra patterns: a new feature descriptor for content-based image retrieval," IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2874-2886, 2012. https://doi.org/10.1109/TIP.2012.2188809
  11. I. J. Jacob, K. G. Srinivasagan, and K. Jayapriya, "Local oppugnant color texture pattern for image retrieval system," Pattern Recognition Letters, vol. 42, pp. 72-78, 2014. https://doi.org/10.1016/j.patrec.2014.01.017
  12. M. Subrahmanyam, R. P. Maheshwari, and R. Balasubramanian, "Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking," Signal Processing, vol. 92, no. 6, pp. 1467-1479, 2012. https://doi.org/10.1016/j.sigpro.2011.12.005
  13. S. K. Vipparthi and S. K. Nagar, "Color directional local quinary patterns for content based indexing and retrieval," Human-centric Computing and Information Sciences, vol. 4, article no. 6, 2014.
  14. L. K. Rao and D. V. Rao, "Local quantized extrema patterns for content-based natural and texture image retrieval," Human-centric Computing and Information Sciences, vol. 5, article no. 26, 2015.
  15. A. Hassan, F. Riaz, and S. Rehman, "Rotation and scale invariant texture classification by compensating for distribution changes using covariate shift in uniform local binary patterns," Electronics Letters, vol. 50, no. 1, pp. 27-29, 2014. https://doi.org/10.1049/el.2013.2578
  16. P. Brodatz, Textures: A Photographic Album for Artist and Designers. New York, NY: Dover Publications, 1966.
  17. University of Southern California, "The USC-SIPI Image Database," [Online]. Available: http://sipi.usc.edu//database/.
  18. MIT Vision and Modeling Group, "Vision texture," [Online]. Available: http://vismod.media.mit.edu/pub/VisTex/.