A B-spline probability density function (PDF) estimation-based texture image identification method is presented in this work, which can be categorized into three processing phases,including PDF feature dictionary learn ing phase,PDF feature representation phase and texture identification phase.In the PDF feature dictionary learning and feature represe ntation phases,anisotropic Gaussian derivative filters are adopted to achieve the image′s spatial textural structures under various Gaussi an observation scales and orientations in advance.Next,the non-parametric PDF feature of the local spatial-structural texture is transfor med to parametric weighting vector estimation based on the pre-fixed B-spline basis functions.The PDF feature dictionary base of the tex ture image samples is obtained by the furthest-neighbor clustering method,and the nearest neighbor method is used successively to obtain the histogram distributions for all kinds of image texture categories for the following texture identification.Finally,the image texture is identified by the simple histogram distance measurement. Abundant confirmative and comparative tests on the commonly-used testing sets i ndicate the effectiveness and superiority of the proposed method .