Texture feature-based language identification using wavelet-domain BDIP, BVLC, and NRMA features

In this paper, we propose a texture feature-based language identification using wavelet-domain BDIP (block difference of inverse probabilities), BVLC (block variance of local correlation coefficients), and NRMA (normalized magnitude) features. The proposed method includes three special operations of NRMA, Donoho´s soft-thresholding, and variance thresholding. In the proposed method, wavelet subbands are first obtained by wavelet transform from a test image and denoised by Donoho´s soft-thresholding. BDIP, BVLC, and NRMA operators are next applied to the wavelet subbands. Moments for each subband of BDIP, BVLC, and NRMA 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 very low feature dimension.