Wavelet domain directional binary pattern using majority principle for texture classification

In this paper, we propose a texture descriptor, called Wavelet Domain Directional Binary Pattern using Majority Principle (WDDBPθ) which integrates the discrete wavelet transform and Local Binary Pattern (LBP). The discrete wavelet transform provides the directional information, while LBP describes the texture features predominately. The proposed approach consists of four phases. In the first phase, the image is subjected to single level decomposition; resulting in discrete wavelet coefficients. The wavelet coefficients present at various distances; one, two and three are compared among themselves which results in sign pattern. In the second phase, the difference of wavelet coefficients located at different distances are compared with global difference of wavelet coefficient and results in magnitude pattern. In the third phase, the majority principle is applied for both sign and magnitude pattern resulting in WDDBP_S and WDDBP_M pattern. Finally, the histogram of both WDDBP_S and WDDBP_M is concatenated. The proposed method is also evaluated by involving the wavelet coefficients present at twelve different directions. The K-NN algorithm is applied to classify the input and database images. The experimental results demonstrate that the proposed method outperforms the other existing methods on Brodatz and Outex database with improved classification accuracy.