The image texture feature reflects some characteristics of the degree of gray distribution, contrast, spatial distribution and changes in the intrinsic properties of image. Under the premise of lower computational complexity, it is a difficult problem for effective feature extraction of deep level image texture. Aiming to solve this problem, this paper, from the analysis of statistical characteristics of adjacent regions, proposes an image texture features extraction method, which is based on Haar local binary pattern (HLBP). In view of simple and quick operating of Haar-like features, effective and reliable to local features statistic, Haar is inducted into LBP. This method first shows eight groups of Haar feature encoding models, which calculate the local texture features of image in accordance with local binary pattern (LBP). Through this method, it can reduce the noise impact effectively. Then, in order to further enhance the effective representations of the image texture features, the method combines with Gabor wavelet filters in different directions and different scales of gray-level image feature extraction, which intends to enhance the effective performance of the texture feature extraction. Finally, through four comparing experiments, this method has proven to be a feasible tool for analyzing image texture features.