Texture Feature-Based Language Identification Using Wavelet-Domain BDIP and BVLC Features and FFT Feature
In this paper, we propose a texture feature-based
language identification using wavelet-domain BDIP (block difference
of inverse probabilities) and BVLC (block variance of local
correlation coefficients) features and FFT (fast Fourier transform)
feature. In the proposed method, wavelet subbands are first obtained
by wavelet transform from a test image and denoised by Donoho-s
soft-thresholding. BDIP and BVLC operators are next applied to the
wavelet subbands. FFT blocks are also obtained by 2D (twodimensional)
FFT from the blocks into which the test image is
partitioned. Some significant FFT coefficients in each block are
selected and magnitude operator is applied to them. Moments for each
subband of BDIP and BVLC and for each magnitude of significant
FFT coefficients are then computed and fused into a feature vector. In
classification, a stabilized Bayesian classifier, which adopts variance
thresholding, searches the training feature vector most similar to the
test feature vector. Experimental results show that the proposed
method with the three operations yields excellent language
identification even with rather low feature dimension.
[1] D. Ghosh, T. Dube, and A. P. Shivaprasad, "Script recognition - a
review," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, Jan. 2010.
[2] J. Hochberg, L. Kerns, P. Kelly, and T. Thomas, "Automatic script
identification from document images using cluster-based templates,"
IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 2, pp. 176-181, Feb.
1997.
[3] A. L. Spitz, "Determination of the script and language content of
document images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no.
3, pp. 235-245, Mar. 1997.
[4] L. Shijian and C. L. Tan, "Script and language identification in noisy and
degraded document images," IEEE Trans. Pattern Anal. Mach. Intell., vol.
30, no. 1, pp. 14-24, Jan. 2008.
[5] G. S. Pearke and T. N. Tan, "Script and language identification from
document images," in Proc. IEEE Workshop on Document Image
Analysis 97, San Juan, Puerto Rico, Jun. 1997, pp. 10-17.
[6] T. N. Tan, "Rotation invariant texture features and their use in automatic
script identification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no.
7, pp. 743-756, Jul. 1998.
[7] W. Chan and G. Coghill, "Text analysis using local energy," Pattern
Recognit., vol. 34, no. 12, pp. 2523-2532, Dec. 2001.
[8] A. Busch, W. W. Boles, and S. Sridharan, "Texture for script
identification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 11,
pp. 1720-1732, Nov. 2005.
[9] W. S. Lee, N. C. Kim, and I. H. Jang, "Texture feature-based language
identification using wavelet-domain BDIP, BVLC, and NRMA features,"
in Proc. IEEE International Workshop on Machine Learning for Signal
Processing 2010, Kittilä, Finland, Aug./Sep. 2010, pp. 444-449.
[10] Y. D. Chun, S. Y. Seo, and N. C. Kim, "Image retrieval using BDIP and
BVLC moments," IEEE Trans. Circuits Syst. Video Technol., vol. 13, no.
9, pp. 951-957, Sep. 2003.
[11] Y. D. Chun, N. C. Kim, I. H. Jang, "Content-based image retrieval using
multiresolution color and texture features," IEEE Trans. Multimedia, vol.
10, no. 6, pp. 1073-1084, Oct. 2008.
[12] H. J. So, M. H. Kim, and N. C. Kim, "Texture classification using
wavelet-domain BDIP and BVLC features," in Proc. 17th European
Signal Processing Conf., Glasgow, Scotland, Aug. 2009, pp. 1117-1120.
[13] H. J. So, M. H. Kim, Y. S. Chung, and N. C. Kim, "Face detection using
sketch operators and vertical symmetry," FAQS-2006, Lecture Notes in
Artificial Intelligence, vol. 4027, pp. 541-551, Jun. 2006.
[14] T. D. Nguyen, S. H. Kim, and N. C. Kim, "An automatic body ROI
determination for 3D visualization of a fetal ultrasound volume,"
KES-2005, Lecture Notes in Artificial Intelligence, vol. 3682, pp. 145-153,
Sep. 2005.
[15] D. L. Donoho, "De-noising by soft-thresholding," IEEE Trans. Inform.
Theory, vol. 41, no. 3, pp. 613-627, May 1995.
[16] R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for
image classification," IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no.
6, pp. 610-621, Nov. 1973.
[17] Q. A. Holmes, D. R. Neusch, and R. A. Shuchman, "Textural analysis and
real-time classification of sea-ice types using digital SAR data," IEEE
Trans. Geosci. Remote Sensing, vol. GE-22, no. 2, pp. 113-120, Mar.
1984.
[18] A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs,
NJ: Prentice-Hall, 1989, ch. 5.
[1] D. Ghosh, T. Dube, and A. P. Shivaprasad, "Script recognition - a
review," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, Jan. 2010.
[2] J. Hochberg, L. Kerns, P. Kelly, and T. Thomas, "Automatic script
identification from document images using cluster-based templates,"
IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 2, pp. 176-181, Feb.
1997.
[3] A. L. Spitz, "Determination of the script and language content of
document images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no.
3, pp. 235-245, Mar. 1997.
[4] L. Shijian and C. L. Tan, "Script and language identification in noisy and
degraded document images," IEEE Trans. Pattern Anal. Mach. Intell., vol.
30, no. 1, pp. 14-24, Jan. 2008.
[5] G. S. Pearke and T. N. Tan, "Script and language identification from
document images," in Proc. IEEE Workshop on Document Image
Analysis 97, San Juan, Puerto Rico, Jun. 1997, pp. 10-17.
[6] T. N. Tan, "Rotation invariant texture features and their use in automatic
script identification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no.
7, pp. 743-756, Jul. 1998.
[7] W. Chan and G. Coghill, "Text analysis using local energy," Pattern
Recognit., vol. 34, no. 12, pp. 2523-2532, Dec. 2001.
[8] A. Busch, W. W. Boles, and S. Sridharan, "Texture for script
identification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 11,
pp. 1720-1732, Nov. 2005.
[9] W. S. Lee, N. C. Kim, and I. H. Jang, "Texture feature-based language
identification using wavelet-domain BDIP, BVLC, and NRMA features,"
in Proc. IEEE International Workshop on Machine Learning for Signal
Processing 2010, Kittilä, Finland, Aug./Sep. 2010, pp. 444-449.
[10] Y. D. Chun, S. Y. Seo, and N. C. Kim, "Image retrieval using BDIP and
BVLC moments," IEEE Trans. Circuits Syst. Video Technol., vol. 13, no.
9, pp. 951-957, Sep. 2003.
[11] Y. D. Chun, N. C. Kim, I. H. Jang, "Content-based image retrieval using
multiresolution color and texture features," IEEE Trans. Multimedia, vol.
10, no. 6, pp. 1073-1084, Oct. 2008.
[12] H. J. So, M. H. Kim, and N. C. Kim, "Texture classification using
wavelet-domain BDIP and BVLC features," in Proc. 17th European
Signal Processing Conf., Glasgow, Scotland, Aug. 2009, pp. 1117-1120.
[13] H. J. So, M. H. Kim, Y. S. Chung, and N. C. Kim, "Face detection using
sketch operators and vertical symmetry," FAQS-2006, Lecture Notes in
Artificial Intelligence, vol. 4027, pp. 541-551, Jun. 2006.
[14] T. D. Nguyen, S. H. Kim, and N. C. Kim, "An automatic body ROI
determination for 3D visualization of a fetal ultrasound volume,"
KES-2005, Lecture Notes in Artificial Intelligence, vol. 3682, pp. 145-153,
Sep. 2005.
[15] D. L. Donoho, "De-noising by soft-thresholding," IEEE Trans. Inform.
Theory, vol. 41, no. 3, pp. 613-627, May 1995.
[16] R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for
image classification," IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no.
6, pp. 610-621, Nov. 1973.
[17] Q. A. Holmes, D. R. Neusch, and R. A. Shuchman, "Textural analysis and
real-time classification of sea-ice types using digital SAR data," IEEE
Trans. Geosci. Remote Sensing, vol. GE-22, no. 2, pp. 113-120, Mar.
1984.
[18] A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs,
NJ: Prentice-Hall, 1989, ch. 5.
@article{"International Journal of Information, Control and Computer Sciences:50502", author = "Ick Hoon Jang and Hoon Jae Lee and Dae Hoon Kwon and Ui Young Pak", title = "Texture Feature-Based Language Identification Using Wavelet-Domain BDIP and BVLC Features and FFT Feature", abstract = "In this paper, we propose a texture feature-based
language identification using wavelet-domain BDIP (block difference
of inverse probabilities) and BVLC (block variance of local
correlation coefficients) features and FFT (fast Fourier transform)
feature. In the proposed method, wavelet subbands are first obtained
by wavelet transform from a test image and denoised by Donoho-s
soft-thresholding. BDIP and BVLC operators are next applied to the
wavelet subbands. FFT blocks are also obtained by 2D (twodimensional)
FFT from the blocks into which the test image is
partitioned. Some significant FFT coefficients in each block are
selected and magnitude operator is applied to them. Moments for each
subband of BDIP and BVLC and for each magnitude of significant
FFT coefficients are then computed and fused into a feature vector. In
classification, a stabilized Bayesian classifier, which adopts variance
thresholding, searches the training feature vector most similar to the
test feature vector. Experimental results show that the proposed
method with the three operations yields excellent language
identification even with rather low feature dimension.", keywords = "BDIP, BVLC, FFT, language identification, texture
feature, wavelet transform.", volume = "7", number = "5", pages = "578-7", }
