Two improved extension of local binary pattern descriptors using wavelet transform for texture classification

Texture image analysis plays a pivotal role in pattern recognition and image retrieval. In this study, two improved local binary pattern descriptors are proposed using wavelet transform for texture analysis. The two proposed methods, namely wavelet domain local statistical binary pattern (WLSBP) and directional WLSBP (dWLSBP α ) both consist of three stages. In the first stage, discrete wavelet decomposition is applied to decompose the image. In the second stage, the proposed statistical parameters are computed from the decomposed image, which results in binary value 0 or 1. Then, the binary values are transformed into WLSBP/dWLSBP α label. In the third stage, the histogram is built using the WLSBP/dWLSBP α labels. The proposed WLSBP and dWLSBP α differ in terms of considering the neighbours. The proposed WLSBP considers the neighbours circularly, whereas dWLSBP α considers the neighbours in the same orientation through the central wavelet coefficient. The proposed approaches have been applied for copy–move forgery detection. Experiments show that the performance of the proposed methods has improved retrieval rate compared with existing methods on both Brodatz and Outex databases.