Texture feature-based language identification using Gabor and MDLC features

In this paper, we propose a texture feature-based language identification using Gabor and MDLC (multi-lag directional local correlation) features. In the proposed method, for a test image, Gabor magnitude images are first obtained by Gabor transform and magnitude operator and MDLC images by MDLC operator. Moments for the Gabor magnitude and MDLC images are then computed and fused into a feature vector. WPCA (whitened principal component analysis) finally searches one of training feature vectors most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification even with low feature dimension due to a well-matched fusion of Gabor and MDLC features.