%0 Journal Article
	%A Ick Hoon Jang and  Hoon Jae Lee and  Dae Hoon Kwon and  Ui Young Pak
	%D 2013
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 77, 2013
	%T Texture Feature-Based Language Identification Using Wavelet-Domain BDIP and BVLC Features and FFT Feature
	%U https://publications.waset.org/pdf/1363
	%V 77
	%X 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.
	%P 632 - 638