Texture Feature-Based Language Identification Using Wavelet-Domain Bdip And Bvlc Features And Fft Feature

• Main Authors: Ick Hoon Jang, Lee, Hoon Jae, Kwon, Dae Hoon, Pak, Ui Young
• Format: Text
• Language: English
• Published: Zenodo 2013
• Subjects: BDIP; BVLC; FFT; language identification; texture feature; wavelet transform.; San Juan; Sea ice
• Online Access: https://dx.doi.org/10.5281/zenodo.1055058; https://zenodo.org/record/1055058

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. : {"references": ["D. Ghosh, T. Dube, and A. P. Shivaprasad, \"Script recognition - a\nreview,\" IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, Jan. 2010.", "J. Hochberg, L. Kerns, P. Kelly, and T. Thomas, \"Automatic script\nidentification from document images using cluster-based templates,\"\nIEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 2, pp. 176-181, Feb.\n1997.", "A. L. Spitz, \"Determination of the script and language content of\ndocument images,\" IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no.\n3, pp. 235-245, Mar. 1997.", "L. Shijian and C. L. Tan, \"Script and language identification in noisy and\ndegraded document images,\" IEEE Trans. Pattern Anal. Mach. Intell., vol.\n30, no. 1, pp. 14-24, Jan. 2008.", "G. S. Pearke and T. N. Tan, \"Script and language identification from\ndocument images,\" in Proc. IEEE Workshop on Document Image\nAnalysis 97, San Juan, Puerto Rico, Jun. 1997, pp. 10-17.", "T. N. Tan, \"Rotation invariant texture features and their use in automatic\nscript identification,\" IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no.\n7, pp. 743-756, Jul. 1998.", "W. Chan and G. Coghill, \"Text analysis using local energy,\" Pattern\nRecognit., vol. 34, no. 12, pp. 2523-2532, Dec. 2001.", "A. Busch, W. W. Boles, and S. Sridharan, \"Texture for script\nidentification,\" IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 11,\npp. 1720-1732, Nov. 2005.", "W. S. Lee, N. C. Kim, and I. H. Jang, \"Texture feature-based language\nidentification using wavelet-domain BDIP, BVLC, and NRMA features,\"\nin Proc. IEEE International Workshop on Machine Learning for Signal\nProcessing 2010, Kittil\u251c\u00f1, Finland, Aug./Sep. 2010, pp. 444-449.\n[10] Y. D. Chun, S. Y. Seo, and N. C. Kim, \"Image retrieval using BDIP and\nBVLC moments,\" IEEE Trans. Circuits Syst. Video Technol., vol. 13, no.\n9, pp. 951-957, Sep. 2003.\n[11] Y. D. Chun, N. C. Kim, I. H. Jang, \"Content-based image retrieval using\nmultiresolution color and texture features,\" IEEE Trans. Multimedia, vol.\n10, no. 6, pp. 1073-1084, Oct. 2008.\n[12] H. J. So, M. H. Kim, and N. C. Kim, \"Texture classification using\nwavelet-domain BDIP and BVLC features,\" in Proc. 17th European\nSignal Processing Conf., Glasgow, Scotland, Aug. 2009, pp. 1117-1120.\n[13] H. J. So, M. H. Kim, Y. S. Chung, and N. C. Kim, \"Face detection using\nsketch operators and vertical symmetry,\" FAQS-2006, Lecture Notes in\nArtificial Intelligence, vol. 4027, pp. 541-551, Jun. 2006.\n[14] T. D. Nguyen, S. H. Kim, and N. C. Kim, \"An automatic body ROI\ndetermination for 3D visualization of a fetal ultrasound volume,\"\nKES-2005, Lecture Notes in Artificial Intelligence, vol. 3682, pp. 145-153,\nSep. 2005.\n[15] D. L. Donoho, \"De-noising by soft-thresholding,\" IEEE Trans. Inform.\nTheory, vol. 41, no. 3, pp. 613-627, May 1995.\n[16] R. M. Haralick, K. Shanmugam, and I. Dinstein, \"Textural features for\nimage classification,\" IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no.\n6, pp. 610-621, Nov. 1973.\n[17] Q. A. Holmes, D. R. Neusch, and R. A. Shuchman, \"Textural analysis and\nreal-time classification of sea-ice types using digital SAR data,\" IEEE\nTrans. Geosci. Remote Sensing, vol. GE-22, no. 2, pp. 113-120, Mar.\n1984.\n[18] A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs,\nNJ: Prentice-Hall, 1989, ch. 5."]}