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Texture Feature-Based Language Identification Using Gabor Feature and Wavelet-Domain BDIP and BVLC Features  

Jang, Ick-Hoon (Department of Digital Electronic Engineering, Kyungwoon University)
Lee, Woo-Shin (School of Electronics Engineering, Kyungpook National University)
Kim, Nam-Chul (School of Electronics Engineering, Kyungpook National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.48, no.4, 2011 , pp. 76-85 More about this Journal
Abstract
In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.
Keywords
Language identification; texture feature; BDIP; BVLC; Gabor transform; wavelet transform;
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